{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer 4.0 Alpha"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/experiment/fsrs4anki_optimizer_alpha.ipynb)\n",
    "\n",
    "↑ Click the above button to open the optimizer on Google Colab.\n",
    "\n",
    "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
    "\n",
    "No need to include media. Need to include scheduling information. \n",
    "\n",
    "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
    "\n",
    "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
    "\n",
    "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
    "\n",
    "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
    "\n",
    "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
    "\n",
    "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Build dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Extract Anki collection & deck file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract successfully!\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "from tqdm import notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import timedelta, datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import sys\n",
    "import torch\n",
    "from torch import nn\n",
    "from sklearn.utils import shuffle\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'./{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Extract successfully!\")\n",
    "\n",
    "\n",
    "notebook.tqdm.pandas()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create time-series feature & analysis\n",
    "\n",
    "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
    "\n",
    "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
    "\n",
    "Then it will generate a concise analysis for your review logs. \n",
    "\n",
    "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
    "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
    "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
    "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
    "- The `factor` is `stability / previous stability`.\n",
    "- The `group_cnt` is the number of review logs that have the same `r_history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "revlog.csv saved.\n",
      "Trainset saved.\n",
      "Retention calculated.\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.7          0.765        1.0     inf       7997\n",
      "            1,3           3.9          0.877        4.2    4.20       4174\n",
      "          1,3,3           8.5          0.882        9.1    2.17       2711\n",
      "        1,3,3,3          17.8          0.860       13.8    1.52       1619\n",
      "      1,3,3,3,3          36.4          0.833       22.3    1.62        845\n",
      "    1,3,3,3,3,3          74.7          0.861       36.4    1.63        402\n",
      "  1,3,3,3,3,3,3         118.2          0.895       38.5    1.06        182\n",
      "              2           1.0          0.901        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.3    7.55        201\n",
      "          2,3,3          11.1          0.890        7.1    0.86        160\n",
      "              3           1.5          0.962        5.4     inf       9134\n",
      "            3,3           3.9          0.966       15.2    2.81       6588\n",
      "          3,3,3           9.0          0.960       23.5    1.55       5166\n",
      "        3,3,3,3          19.1          0.941       43.5    1.85       3542\n",
      "      3,3,3,3,3          35.9          0.931       53.0    1.22       1976\n",
      "    3,3,3,3,3,3          65.0          0.928       97.6    1.84       1149\n",
      "  3,3,3,3,3,3,3         100.9          0.947      142.9    1.46        537\n",
      "3,3,3,3,3,3,3,3         133.6          0.990     1821.9   12.75        131\n",
      "              4           3.8          0.966       12.1     inf      11599\n",
      "            4,3           8.1          0.975       39.0    3.22       7515\n",
      "          4,3,3          17.8          0.963       56.5    1.45       5307\n",
      "        4,3,3,3          32.1          0.952       82.6    1.46       3032\n",
      "      4,3,3,3,3          46.3          0.956      125.4    1.52       1380\n",
      "    4,3,3,3,3,3          67.5          0.956      115.3    0.92        534\n",
      "  4,3,3,3,3,3,3          78.3          0.978      117.5    1.02        253\n",
      "4,3,3,3,3,3,3,3         114.1          0.984      177.8    1.51        166\n",
      "Analysis saved!\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(\"SELECT * FROM revlog\")\n",
    "revlog = res.fetchall()\n",
    "\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl',\n",
    "              'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000) &\n",
    "        (df['r'] > 0)].copy()\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "df = df[(df['type'] != 3) | (df['factor'] != 0)].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=next_day_starts_at)\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['delta_t'] = df['delta_t'].astype(dtype=int)\n",
    "\n",
    "df['i'] = df.groupby('cid').cumcount() + 1\n",
    "df.loc[df['i'] == 1, 'delta_t'] = 0\n",
    "\n",
    "\n",
    "df = df.groupby('cid').filter(lambda group: group['type'].iloc[0] == 0)\n",
    "df['prev_type'] = df.groupby('cid')['type'].shift(1).fillna(0).astype(int)\n",
    "df['helper'] = (df['type'] == 0) & ((df['prev_type'] == 1) | (df['prev_type'] == 2)) & (df['i'] > 1)\n",
    "df['helper'] = df['helper'].astype(int)\n",
    "df['helper'] = df.groupby('cid')['helper'].cumsum()\n",
    "df = df[df['helper'] == 0]\n",
    "del df['prev_type']\n",
    "del df['helper']\n",
    "from itertools import accumulate\n",
    "\n",
    "def cum_concat(x):\n",
    "    return list(accumulate(x))\n",
    "\n",
    "t_history = df.groupby('cid', group_keys=False)['delta_t'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['t_history']=[','.join(map(str, item[:-1])) for sublist in t_history for item in sublist]\n",
    "r_history = df.groupby('cid', group_keys=False)['r'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['r_history']=[','.join(map(str, item[:-1])) for sublist in r_history for item in sublist]\n",
    "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "df['y'] = df['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x])\n",
    "df['retention'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['y'].transform('mean')\n",
    "df['total_cnt'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['id'].transform('count')\n",
    "print(\"Retention calculated.\")\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history', 'y'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in notebook.tqdm(df.index):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)]\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "    print(df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False))\n",
    "    print(\"Analysis saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Optimize parameter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define the model\n",
    "\n",
    "FSRS is a time-series model for predicting memory states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 3, 1, 1, 0.2, 6, 0.12, 1, 2, 0.2, 0.2, 1, 1, 1, 2, 3, 4]\n",
    "'''\n",
    "w[0]: initial_stability_for_again_answer\n",
    "w[1]: initial_stability_step_per_rating\n",
    "w[2]: initial_difficulty_for_good_answer\n",
    "w[3]: initial_difficulty_step_per_rating\n",
    "w[4]: next_difficulty_step_per_rating\n",
    "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
    "w[6]: next_stability_factor_after_success\n",
    "w[7]: next_stability_stabilization_decay_after_success\n",
    "w[8]: next_stability_retrievability_gain_after_success\n",
    "w[9]: next_stability_factor_after_failure\n",
    "w[10]: next_stability_difficulty_decay_after_success\n",
    "w[11]: next_stability_stability_gain_after_failure\n",
    "w[12]: next_stability_retrievability_gain_after_failure\n",
    "For more details about the parameters, please see: \n",
    "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
    "'''\n",
    "d_max = 15\n",
    "\n",
    "def forgetting_curve(t, s):\n",
    "    return (1 + t / (9 * s)) ** -1 # power forgetting curve\n",
    "    # return 0.9 ** (t / s) # exponential forgetting curve\n",
    "\n",
    "def next_interval(stability, retention):\n",
    "    return max(1,round(9 * stability * (1/retention - 1)))\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w, dtype=torch.float32))\n",
    "\n",
    "    def stability_after_success(self, s, new_d, r):\n",
    "        new_s = s * (1 + self.w[6] *\n",
    "                        (10 * torch.pow(new_d, -self.w[13])) *\n",
    "                        torch.pow(s, -self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "    \n",
    "    def stability_after_failure(self, s, new_d, r):\n",
    "        new_s = self.w[9] * \\\n",
    "                torch.pow(new_d, -self.w[10]) * \\\n",
    "                (torch.pow(s + 1, self.w[11]) - 1) * \\\n",
    "                torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def step(self, X, state):\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        keys = torch.tensor([1, 2, 3, 4])\n",
    "        keys = keys.view(1, -1).expand(X[:,1].long().size(0), -1)\n",
    "        index = (X[:,1].long().unsqueeze(1) == keys).nonzero(as_tuple=True)\n",
    "        X_rating = X[:,1].clone().float()\n",
    "        X_rating[index[0]] = self.w[14+index[1]]\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X_rating - self.w[14])\n",
    "            new_d = self.w[2] - self.w[3] * (X_rating - self.w[16])\n",
    "            new_d = new_d.clamp(1, d_max)\n",
    "        else:\n",
    "            r = forgetting_curve(X[:,0], state[:,0])\n",
    "            new_d = state[:,1] - self.w[4] * (X_rating - self.w[16])\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, d_max)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state[:,0], new_d, r), self.stability_after_failure(state[:,0], new_d, r))\n",
    "        new_s = new_s.clamp(0.1, 36500)\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs, state=None):\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        else:\n",
    "            state, = state\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init, current):\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "\n",
    "class WeightClipper(object):\n",
    "    def __init__(self, frequency=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, d_max)\n",
    "            w[3] = w[3].clamp(0.1, 5)\n",
    "            w[4] = w[4].clamp(0.1, 5)\n",
    "            w[5] = w[5].clamp(0.05, 0.5)\n",
    "            w[6] = w[6].clamp(0, 10)\n",
    "            w[7] = w[7].clamp(0.15, 0.8)\n",
    "            w[8] = w[8].clamp(0.01, 2)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(0.01, 2)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            w[13] = w[13].clamp(0.1, 2)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line):\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from typing import List\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "    \n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source, batch_size):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0 and remainder > 0:\n",
    "            self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        elif full_batches > 0 and remainder == 0:\n",
    "            self.batch_indices = np.split(indices, full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from (self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist())\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Optimizer(object):\n",
    "    def __init__(self, train_set, test_set, n_epoch=1, lr=1e-2, batch_size=256) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.pre_train_data_loader.batch_sampler.batch_nums + self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set, test_set):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self):\n",
    "        # pretrain\n",
    "\n",
    "        epoch_len = len(self.train_data_loader)\n",
    "        pbar = notebook.tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "\n",
    "        for k in range(self.n_epoch):\n",
    "            self.eval()\n",
    "\n",
    "            for i, batch in enumerate(self.pre_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = forgetting_curve(delta_ts, stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(n=real_batch_size)\n",
    "\n",
    "            for name, param in self.model.named_parameters():\n",
    "                print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "            \n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = forgetting_curve(delta_ts, stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "                \n",
    "            for name, param in self.model.named_parameters():\n",
    "                print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "                \n",
    "        self.eval()\n",
    "\n",
    "        w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "        return w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sequences, delta_ts, labels, seq_lens = self.train_set.x_train, self.train_set.t_train, self.train_set.y_train, self.train_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = forgetting_curve(delta_ts, stabilities)\n",
    "            loss = self.loss_fn(retentions, labels)\n",
    "            self.avg_train_losses.append(loss/len(self.train_set))\n",
    "            print(f\"Loss in trainset: {self.avg_train_losses[-1]:.4f}\")\n",
    "            \n",
    "            sequences, delta_ts, labels, seq_lens = self.test_set.x_train, self.test_set.t_train, self.test_set.y_train, self.test_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = forgetting_curve(delta_ts, stabilities)\n",
    "            loss = self.loss_fn(retentions, labels)\n",
    "            self.avg_eval_losses.append(loss/len(self.test_set))\n",
    "            print(f\"Loss in testset: {self.avg_eval_losses[-1]:.4f}\")\n",
    "\n",
    "    def plot(self):\n",
    "        plt.plot(self.avg_train_losses, label='train')\n",
    "        plt.plot(self.avg_eval_losses, label='test')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.ylabel('loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Train the model\n",
    "\n",
    "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b232e6b5540468c90a430a817a2fade",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/223795 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n",
      "TRAIN: 178072 TEST: 45723\n",
      "dataset built\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bd4d734253b42d796721b8aee7e37d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/890360 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3399\n",
      "Loss in testset: 0.2957\n",
      "w: [0.8905, 1.4268, 3.0, 1.0, 1.0, 0.2, 6.0, 0.15, 1.0, 2.0, 0.2, 0.2, 1.0, 1.0, 0.5952, 1.7269, 3.4454, 4.0]\n",
      "w: [0.9278, 1.5906, 1.8639, 0.7346, 1.0904, 0.05, 6.3386, 0.2134, 1.3463, 2.1119, 0.0643, 0.4137, 1.0158, 1.3443, 0.2374, 1.5476, 3.3619, 4.7004]\n",
      "Loss in trainset: 0.3221\n",
      "Loss in testset: 0.2761\n",
      "w: [0.8671, 1.6006, 1.7846, 0.7181, 1.1276, 0.05, 6.3277, 0.2377, 1.3371, 2.1111, 0.0654, 0.4213, 1.0289, 1.3647, 0.2788, 1.4086, 3.3674, 4.7446]\n",
      "w: [0.8657, 1.7998, 1.9595, 0.9553, 1.012, 0.1452, 6.3161, 0.2223, 1.3547, 2.0414, 0.0873, 0.4269, 1.0397, 1.3915, 0.0351, 1.3521, 3.5537, 4.6803]\n",
      "Loss in trainset: 0.3232\n",
      "Loss in testset: 0.2763\n",
      "w: [0.8787, 1.42, 1.9629, 0.93, 1.0237, 0.1589, 6.3444, 0.1875, 1.3806, 2.0162, 0.1117, 0.4116, 1.042, 1.3953, 0.2848, 1.2618, 3.4397, 4.7048]\n",
      "w: [0.947, 1.3365, 1.8741, 0.8575, 0.9542, 0.1089, 6.3579, 0.2253, 1.3671, 2.003, 0.1044, 0.4191, 0.8985, 1.4552, 0.1844, 1.4997, 3.4107, 4.7103]\n",
      "Loss in trainset: 0.3218\n",
      "Loss in testset: 0.2762\n",
      "w: [0.8362, 1.3442, 1.8887, 0.8513, 0.9782, 0.0833, 6.3497, 0.2254, 1.3577, 1.9966, 0.1091, 0.4106, 0.8942, 1.4688, 0.205, 1.4341, 3.4132, 4.7362]\n",
      "w: [0.8478, 1.3324, 1.7942, 0.8075, 0.9171, 0.0979, 6.378, 0.2101, 1.3656, 2.0044, 0.0749, 0.3959, 0.8214, 1.4855, 0.1514, 1.5566, 3.4164, 4.6842]\n",
      "Loss in trainset: 0.3214\n",
      "Loss in testset: 0.2761\n",
      "w: [0.844, 1.3364, 1.7802, 0.7878, 0.9079, 0.104, 6.3905, 0.2045, 1.3777, 2.017, 0.0647, 0.4095, 0.8297, 1.4748, 0.1707, 1.5525, 3.4056, 4.6888]\n",
      "w: [0.8414, 1.3425, 1.7951, 0.7908, 0.9152, 0.0895, 6.3734, 0.2308, 1.358, 2.0118, 0.0705, 0.4001, 0.8066, 1.4935, 0.1546, 1.5465, 3.4226, 4.673]\n",
      "Loss in trainset: 0.3213\n",
      "Loss in testset: 0.2761\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 181664 TEST: 42131\n",
      "dataset built\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a57f1fec04e4182b1d9ed1ba3038fb6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/908320 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3199\n",
      "Loss in testset: 0.3782\n",
      "w: [1.6272, 1.7625, 3.0, 1.0, 1.0, 0.2, 6.0, 0.15, 1.0, 2.0, 0.2, 0.2, 1.0, 1.0, 0.3443, 1.6885, 3.2749, 5.0654]\n",
      "w: [1.6694, 1.8424, 1.9857, 0.6681, 1.0651, 0.05, 6.2606, 0.1983, 1.277, 1.9897, 0.1054, 0.3535, 1.0815, 1.2824, 0.1131, 1.6067, 3.5672, 4.736]\n",
      "Loss in trainset: 0.3024\n",
      "Loss in testset: 0.3677\n",
      "w: [1.4137, 1.7045, 2.0865, 0.7971, 1.0971, 0.05, 6.1823, 0.2806, 1.2065, 1.9991, 0.098, 0.3611, 1.0783, 1.3432, 0.2956, 1.4094, 3.4186, 4.9864]\n",
      "w: [1.4609, 1.8598, 2.1994, 0.8965, 1.0131, 0.1706, 6.2923, 0.1564, 1.3269, 1.9286, 0.066, 0.4229, 1.0671, 1.2947, 0.1149, 1.4875, 3.5757, 4.7983]\n",
      "Loss in trainset: 0.3073\n",
      "Loss in testset: 0.3702\n",
      "w: [1.3277, 1.8928, 2.2436, 1.0535, 1.0466, 0.1466, 6.2251, 0.1841, 1.2634, 1.9334, 0.0551, 0.4355, 1.0868, 1.3681, 0.1656, 1.35, 3.4453, 5.1576]\n",
      "w: [1.3442, 1.9623, 2.0641, 0.7918, 0.9182, 0.1286, 6.2464, 0.2394, 1.2508, 1.9062, 0.0191, 0.4253, 0.9378, 1.4097, 0.1991, 1.4888, 3.4933, 4.8624]\n",
      "Loss in trainset: 0.3028\n",
      "Loss in testset: 0.3643\n",
      "w: [1.2063, 1.9125, 2.0318, 0.7736, 0.9247, 0.1357, 6.2653, 0.2223, 1.2686, 1.9071, 0.0145, 0.4238, 0.9331, 1.3999, 0.2975, 1.418, 3.4069, 4.9863]\n",
      "w: [1.1647, 1.8969, 1.9726, 0.7475, 0.9102, 0.0872, 6.2477, 0.2018, 1.2304, 1.8628, 0.0296, 0.3873, 0.8179, 1.436, 0.2529, 1.516, 3.458, 4.8525]\n",
      "Loss in trainset: 0.3010\n",
      "Loss in testset: 0.3609\n",
      "w: [1.1291, 1.8878, 1.9653, 0.7397, 0.9077, 0.0873, 6.2531, 0.1947, 1.2354, 1.8696, 0.0237, 0.3948, 0.8224, 1.4333, 0.2772, 1.504, 3.435, 4.8823]\n",
      "w: [1.1247, 1.891, 1.9669, 0.7464, 0.905, 0.0889, 6.2457, 0.1932, 1.2248, 1.8606, 0.0304, 0.3858, 0.802, 1.4435, 0.2631, 1.5236, 3.4455, 4.8614]\n",
      "Loss in trainset: 0.3009\n",
      "Loss in testset: 0.3605\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 184379 TEST: 39416\n",
      "dataset built\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b479a473edd446686e74459ffa3acbc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/921895 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3329\n",
      "Loss in testset: 0.3213\n",
      "w: [0.9614, 1.866, 3.0, 1.0, 1.0, 0.2, 6.0, 0.15, 1.0, 2.0, 0.2, 0.2, 1.0, 1.0, 0.1685, 2.0, 3.3583, 5.0397]\n",
      "w: [1.0393, 1.7801, 2.2069, 0.9034, 1.1691, 0.0653, 6.2075, 0.2635, 1.2553, 1.9807, 0.1122, 0.3637, 1.2126, 1.3314, 0.108, 1.5432, 3.5154, 4.869]\n",
      "Loss in trainset: 0.3169\n",
      "Loss in testset: 0.3057\n",
      "w: [0.7858, 1.9157, 2.1495, 0.8364, 1.1635, 0.0961, 6.2778, 0.1637, 1.3259, 1.9738, 0.1081, 0.3532, 1.1735, 1.2824, 0.161, 1.5387, 3.3049, 5.2879]\n",
      "w: [0.5169, 1.9031, 1.8053, 0.5001, 0.9869, 0.0854, 6.2976, 0.1688, 1.397, 2.085, 0.0293, 0.3725, 1.2438, 1.4087, 0.1021, 1.7351, 3.4584, 4.8353]\n",
      "Loss in trainset: 0.3165\n",
      "Loss in testset: 0.3038\n",
      "w: [0.8127, 1.8493, 1.6236, 0.5512, 0.9708, 0.0889, 6.367, 0.15, 1.4573, 2.1166, 0.01, 0.4059, 1.2439, 1.3895, 0.2479, 1.8004, 3.1927, 5.2138]\n",
      "w: [0.6202, 1.9164, 1.9577, 0.7801, 0.891, 0.0905, 6.2554, 0.2326, 1.3732, 2.0706, 0.0594, 0.371, 1.097, 1.4741, 0.098, 1.7623, 3.4487, 4.835]\n",
      "Loss in trainset: 0.3142\n",
      "Loss in testset: 0.3024\n",
      "w: [0.7969, 1.8869, 1.9723, 0.7889, 0.8836, 0.0929, 6.2484, 0.2424, 1.365, 2.0743, 0.0543, 0.3682, 1.1052, 1.4776, 0.1658, 1.7522, 3.3616, 4.9678]\n",
      "w: [0.7977, 1.9425, 1.9477, 0.7188, 0.8844, 0.0813, 6.243, 0.2133, 1.3423, 2.0712, 0.0453, 0.3664, 0.995, 1.5076, 0.1346, 1.7324, 3.4454, 4.819]\n",
      "Loss in trainset: 0.3139\n",
      "Loss in testset: 0.3023\n",
      "w: [0.8057, 1.9395, 1.9469, 0.7221, 0.8869, 0.0814, 6.2398, 0.2133, 1.3384, 2.0836, 0.0336, 0.3799, 0.998, 1.5127, 0.145, 1.722, 3.4311, 4.8491]\n",
      "w: [0.8076, 1.9415, 1.9328, 0.7157, 0.8824, 0.0827, 6.2411, 0.2007, 1.3371, 2.0736, 0.0391, 0.3695, 0.9857, 1.5149, 0.1437, 1.7229, 3.4422, 4.8223]\n",
      "Loss in trainset: 0.3139\n",
      "Loss in testset: 0.3022\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 175532 TEST: 48263\n",
      "dataset built\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b07ae5873234dfda11b9d523643c052",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/877660 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3340\n",
      "Loss in testset: 0.3193\n",
      "w: [0.8705, 1.9109, 3.0, 1.0, 1.0, 0.2, 6.0, 0.15, 1.0, 2.0, 0.2, 0.2, 1.0, 1.0, 0.0968, 1.4593, 3.0, 4.9445]\n",
      "w: [0.8399, 1.9442, 2.0688, 0.9653, 0.9383, 0.1124, 6.1529, 0.2334, 1.2085, 2.143, 0.0275, 0.4282, 1.112, 1.3593, -0.1535, 1.2589, 3.4123, 4.6532]\n",
      "Loss in trainset: 0.3166\n",
      "Loss in testset: 0.3036\n",
      "w: [0.9145, 2.1363, 2.1386, 1.0095, 1.005, 0.0874, 6.0953, 0.2733, 1.1532, 2.1483, 0.0214, 0.4416, 1.1035, 1.3978, -0.2148, 0.9824, 3.4803, 4.7958]\n",
      "w: [0.7709, 2.1831, 2.0505, 0.7603, 1.0128, 0.0538, 6.2008, 0.208, 1.311, 2.0416, 0.1159, 0.3918, 1.2417, 1.4388, -0.111, 1.1554, 3.2972, 4.9096]\n",
      "Loss in trainset: 0.3167\n",
      "Loss in testset: 0.3032\n",
      "w: [0.9226, 2.2193, 2.0291, 0.6953, 1.018, 0.0758, 6.2247, 0.183, 1.335, 2.0581, 0.1052, 0.4053, 1.2365, 1.4188, -0.0086, 0.9911, 3.2613, 4.9894]\n",
      "w: [0.9813, 2.2654, 1.9271, 0.6886, 0.88, 0.0741, 6.1767, 0.2417, 1.2693, 2.0266, 0.0604, 0.3972, 0.9525, 1.4153, -0.0254, 1.2589, 3.2741, 4.7658]\n",
      "Loss in trainset: 0.3149\n",
      "Loss in testset: 0.3024\n",
      "w: [0.8578, 2.3139, 1.8384, 0.6387, 0.8488, 0.0968, 6.2258, 0.1945, 1.3168, 2.0807, 0.0139, 0.4531, 0.9775, 1.382, 0.0421, 1.1951, 3.2254, 4.8454]\n",
      "w: [0.8316, 2.2722, 1.9462, 0.6632, 0.8569, 0.0794, 6.1627, 0.2014, 1.2422, 2.019, 0.0579, 0.3863, 0.8435, 1.458, -0.0235, 1.3113, 3.321, 4.6086]\n",
      "Loss in trainset: 0.3147\n",
      "Loss in testset: 0.3019\n",
      "w: [0.8236, 2.2845, 1.9652, 0.6654, 0.8594, 0.0785, 6.1517, 0.2086, 1.2307, 2.019, 0.0569, 0.3864, 0.8392, 1.4659, -0.0268, 1.2926, 3.3323, 4.6027]\n",
      "w: [0.8281, 2.2937, 1.9464, 0.6439, 0.8405, 0.0842, 6.1698, 0.1875, 1.2459, 2.0222, 0.0547, 0.3873, 0.8292, 1.4557, -0.0141, 1.3186, 3.3122, 4.6105]\n",
      "Loss in trainset: 0.3146\n",
      "Loss in testset: 0.3019\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 175533 TEST: 48262\n",
      "dataset built\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82490cc1c18240ec927fdfe701ac1b47",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/877665 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3277\n",
      "Loss in testset: 0.3421\n",
      "w: [0.9303, 1.8289, 3.0, 1.0, 1.0, 0.2, 6.0, 0.15, 1.0, 2.0, 0.2, 0.2, 1.0, 1.0, 0.2728, 1.3553, 3.2512, 5.0769]\n",
      "w: [0.9578, 1.7711, 1.986, 0.9478, 1.0328, 0.1249, 6.3395, 0.2174, 1.3855, 2.1438, 0.1383, 0.5051, 1.376, 1.461, 0.0707, 1.4548, 3.4111, 4.993]\n",
      "Loss in trainset: 0.3111\n",
      "Loss in testset: 0.3277\n",
      "w: [0.7701, 1.8981, 1.9989, 0.9299, 1.0295, 0.1616, 6.2986, 0.2786, 1.3485, 2.1426, 0.1407, 0.5105, 1.3605, 1.4594, 0.2096, 1.2502, 3.2189, 5.4068]\n",
      "w: [0.4736, 1.9935, 1.8546, 0.5175, 1.0503, 0.0856, 6.4258, 0.2259, 1.5442, 2.0786, 0.12, 0.3618, 1.5586, 1.4298, -0.0609, 1.5181, 3.504, 4.9192]\n",
      "Loss in trainset: 0.3106\n",
      "Loss in testset: 0.3253\n",
      "w: [0.845, 1.8169, 1.8119, 0.424, 1.0307, 0.1183, 6.4765, 0.1992, 1.5931, 2.1298, 0.0651, 0.412, 1.6078, 1.3842, 0.173, 1.4202, 3.3469, 5.05]\n",
      "w: [0.5722, 1.9208, 2.111, 0.7597, 0.9671, 0.1197, 6.3505, 0.1761, 1.4721, 2.0669, 0.1178, 0.4, 1.4787, 1.5234, -0.1004, 1.5879, 3.568, 4.7356]\n",
      "Loss in trainset: 0.3092\n",
      "Loss in testset: 0.3251\n",
      "w: [0.7817, 1.8246, 2.07, 0.7965, 0.9653, 0.1042, 6.3682, 0.15, 1.4859, 2.0459, 0.1369, 0.3793, 1.4256, 1.5249, -0.0012, 1.5576, 3.4499, 4.9092]\n",
      "w: [0.807, 1.8818, 2.1098, 0.707, 0.9012, 0.1063, 6.349, 0.2036, 1.4533, 2.0789, 0.0925, 0.4241, 1.3335, 1.5333, -0.0313, 1.6247, 3.5149, 4.7352]\n",
      "Loss in trainset: 0.3088\n",
      "Loss in testset: 0.3249\n",
      "w: [0.8213, 1.8739, 2.1101, 0.6956, 0.8986, 0.1124, 6.3516, 0.2082, 1.456, 2.0781, 0.0929, 0.4225, 1.3363, 1.529, -0.0021, 1.6052, 3.4877, 4.7757]\n",
      "w: [0.8137, 1.872, 2.0928, 0.7043, 0.9114, 0.0962, 6.3521, 0.1938, 1.453, 2.0562, 0.113, 0.4041, 1.3001, 1.5412, -0.0128, 1.6095, 3.4902, 4.7807]\n",
      "Loss in trainset: 0.3084\n",
      "Loss in testset: 0.3243\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['y'] = dataset['r'].map({1: 0, 2: 1, 3: 1, 4: 1})\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "w = []\n",
    "\n",
    "sgkf = StratifiedGroupKFold(n_splits=5)\n",
    "for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "    train_set = dataset.iloc[train_index].copy()\n",
    "    test_set = dataset.iloc[test_index].copy()\n",
    "    optimizer = Optimizer(train_set, test_set, n_epoch=5, lr=4e-2, batch_size=512)\n",
    "    w.append(optimizer.train())\n",
    "    optimizer.plot()\n",
    "\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Result\n",
    "\n",
    "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8831, 1.8681, 1.9468, 0.7202, 0.8909, 0.0883, 6.2764, 0.2012, 1.3238, 2.0049, 0.0615, 0.3894, 0.9447, 1.4898, 0.1069, 1.5442, 3.4225, 4.7496]\n"
     ]
    }
   ],
   "source": [
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "print(avg_w.tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preview\n",
    "\n",
    "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,1d,2d,4d,8d,15d,27d,1.6m,2.7m,4.6m,7.6m\n",
      "difficulty history: 0,4.3,4.1,3.9,3.8,3.6,3.5,3.3,3.2,3.1,3.0\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,4d,8d,17d,1.1m,2.2m,4.0m,7.0m,11.9m,1.6y,2.6y\n",
      "difficulty history: 0,3.3,3.2,3.1,3.0,2.9,2.8,2.7,2.7,2.6,2.5\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,7d,23d,2.1m,5.1m,11.2m,1.9y,3.5y,6.2y,10.6y,17.2y\n",
      "difficulty history: 0,1.9,1.9,1.9,1.9,1.9,1.9,1.9,1.9,1.9,1.9\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,10d,2.0m,8.2m,2.1y,5.5y,12.3y,24.5y,44.5y,75.4y,100.0y\n",
      "difficulty history: 0,1.0,1.1,1.2,1.2,1.3,1.4,1.4,1.5,1.5,1.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w):\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history, r_history):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "        \n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        with torch.no_grad():\n",
    "            outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "            stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "            return stabilities.tolist(), difficulties.tolist()\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "for first_rating in (1,2,3,4):\n",
    "    print(f'first rating: {first_rating}')\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = next_interval(float(states[0]), requestRetention)\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    print(f\"rating history: {r_history}\")\n",
    "    print(\"interval history: \" + \",\".join([f\"{ivl}d\" if ivl < 30 else f\"{ivl / 30:.1f}m\" if ivl < 365 else f\"{ivl / 365:.1f}y\" for ivl in map(int, t_history.split(','))]))\n",
    "    print(f\"difficulty history: {d_history}\")\n",
    "    print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([7.0770, 1.9468])\n",
      "tensor([22.6316,  1.9468])\n",
      "tensor([63.0377,  1.9468])\n",
      "tensor([153.1609,   1.9468])\n",
      "tensor([336.2332,   1.9468])\n",
      "tensor([17.3361,  4.6398])\n",
      "tensor([4.1033, 7.0951])\n",
      "tensor([5.6984, 6.6405])\n",
      "tensor([8.1564, 6.2260])\n",
      "tensor([11.5131,  5.8482])\n",
      "tensor([16.6431,  5.5037])\n",
      "tensor([24.0147,  5.1896])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,7,23,63,153,336,17,4,6,8,12,17,24\n",
      "difficulty history: 0,1.9,1.9,1.9,1.9,1.9,4.6,7.1,6.6,6.2,5.8,5.5,5.2\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = next_interval(float(states[0]), requestRetention)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "print(f\"rating history: {test_rating_sequence}\")\n",
    "print(f\"interval history: {t_history}\")\n",
    "print(f\"difficulty history: {d_history}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Predict memory states and distribution of difficulty\n",
    "\n",
    "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
    "\n",
    "Meanwhile, it will count the distribution of difficulty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction.tsv saved.\n",
      "difficulty\n",
      "1     0.193977\n",
      "2     0.134945\n",
      "3     0.079546\n",
      "4     0.132715\n",
      "5     0.072835\n",
      "6     0.084770\n",
      "7     0.078876\n",
      "8     0.067115\n",
      "9     0.052526\n",
      "10    0.038240\n",
      "11    0.025975\n",
      "12    0.017127\n",
      "13    0.010599\n",
      "14    0.006448\n",
      "15    0.004308\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "stabilities = map(lambda x: round(x, 2), stabilities)\n",
    "difficulties = map(lambda x: round(x, 2), difficulties)\n",
    "dataset['stability'] = list(stabilities)\n",
    "dataset['difficulty'] = list(difficulties)\n",
    "prediction = dataset.groupby(by=['t_history', 'r_history']).agg({\"stability\": \"mean\", \"difficulty\": \"mean\", \"id\": \"count\"})\n",
    "prediction.reset_index(inplace=True)\n",
    "prediction.sort_values(by=['r_history'], inplace=True)\n",
    "prediction.rename(columns={\"id\": \"count\"}, inplace=True)\n",
    "prediction.to_csv(\"./prediction.tsv\", sep='\\t', index=None)\n",
    "print(\"prediction.tsv saved.\")\n",
    "prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x)))\n",
    "difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum()\n",
    "print(difficulty_distribution)\n",
    "difficulty_distribution_padding = np.zeros(d_max)\n",
    "for i in range(d_max):\n",
    "    if i+1 in difficulty_distribution.index:\n",
    "        difficulty_distribution_padding[i] = difficulty_distribution.loc[i+1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Optimize retention to minimize the time of reviews\n",
    "\n",
    "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
    "\n",
    "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average time for failed cards: 25.0s\n",
      "average time for recalled cards: 8.0s\n",
      "terminal stability:  100.18\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db0cea1ae6734dc5b2a01716b2f95af4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected_time.csv saved.\n",
      "\n",
      "-----suggested retention (experimental): 0.80-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = 1.01\n",
    "index_len = 664\n",
    "index_offset = 200\n",
    "d_range = d_max\n",
    "d_offset = 1\n",
    "r_time = 8\n",
    "f_time = 25\n",
    "max_time = 200000\n",
    "\n",
    "type_block = dict()\n",
    "type_count = dict()\n",
    "type_time = dict()\n",
    "last_t = type_sequence[0]\n",
    "type_block[last_t] = 1\n",
    "type_count[last_t] = 1\n",
    "type_time[last_t] = time_sequence[0]\n",
    "for i,t in enumerate(type_sequence[1:]):\n",
    "    type_count[t] = type_count.setdefault(t, 0) + 1\n",
    "    type_time[t] = type_time.setdefault(t, 0) + time_sequence[i]\n",
    "    if t != last_t:\n",
    "        type_block[t] = type_block.setdefault(t, 0) + 1\n",
    "    last_t = t\n",
    "\n",
    "r_time = round(type_time[1]/type_count[1]/1000, 1)\n",
    "\n",
    "if 2 in type_count and 2 in type_block:\n",
    "    f_time = round(type_time[2]/type_block[2]/1000 + r_time, 1)\n",
    "\n",
    "print(f\"average time for failed cards: {f_time}s\")\n",
    "print(f\"average time for recalled cards: {r_time}s\")\n",
    "\n",
    "def stability2index(stability):\n",
    "    return int(round(np.log(stability) / np.log(base)) + index_offset)\n",
    "\n",
    "def init_stability(d):\n",
    "    return max(((d - avg_w[2]) / -avg_w[3] + avg_w[16] - avg_w[14]) * avg_w[1] + avg_w[0], np.power(base, -index_offset))\n",
    "\n",
    "def post_lapse_stability_bonus(s):\n",
    "    offset = np.power(1/avg_w[11],1/(avg_w[11]-1))\n",
    "    return np.power(s+offset, avg_w[11]) - np.power(offset, avg_w[11])\n",
    "\n",
    "def cal_next_recall_stability(s, r, d, response):\n",
    "    if response == 1:\n",
    "        return s * (1 + avg_w[6] * 10 * np.power(d, -avg_w[13]) * np.power(s, -avg_w[7]) * (np.exp((1 - r) * avg_w[8]) - 1))\n",
    "    else:\n",
    "        return avg_w[9] * np.power(d, -avg_w[10]) * post_lapse_stability_bonus(s) * np.exp((1 - r) * avg_w[12])\n",
    "\n",
    "\n",
    "stability_list = np.array([np.power(base, i - index_offset) for i in range(index_len)])\n",
    "print(f\"terminal stability: {stability_list.max(): .2f}\")\n",
    "df = pd.DataFrame(columns=[\"retention\", \"difficulty\", \"time\"])\n",
    "\n",
    "for percentage in notebook.tqdm(range(96, 66, -2)):\n",
    "    recall = percentage / 100\n",
    "    time_list = np.zeros((d_range, index_len))\n",
    "    time_list[:,:-1] = max_time\n",
    "    for d in range(d_range, 0, -1):\n",
    "        s0 = init_stability(d)\n",
    "        s0_index = stability2index(s0)\n",
    "        diff = max_time\n",
    "        while diff > 0.1:\n",
    "            s0_time = time_list[d - 1][s0_index]\n",
    "            for s_index in range(index_len - 2, -1, -1):\n",
    "                stability = stability_list[s_index]\n",
    "                interval = max(1, round(stability * np.log(recall) / np.log(0.9)))\n",
    "                p_recall = forgetting_curve(interval, stability)\n",
    "                recall_s = cal_next_recall_stability(stability, p_recall, d, 1)\n",
    "                forget_d = min(d + d_offset, d_max)\n",
    "                forget_s = cal_next_recall_stability(stability, p_recall, forget_d, 0)\n",
    "                recall_s_index = min(stability2index(recall_s), index_len - 1)\n",
    "                forget_s_index = min(max(stability2index(forget_s), 0), index_len - 1)\n",
    "                recall_time = time_list[d - 1][recall_s_index] + r_time\n",
    "                forget_time = time_list[forget_d - 1][forget_s_index] + f_time\n",
    "                exp_time = p_recall * recall_time + (1.0 - p_recall) * forget_time\n",
    "                if exp_time < time_list[d - 1][s_index]:\n",
    "                    time_list[d - 1][s_index] = exp_time\n",
    "            diff = s0_time - time_list[d - 1][s0_index]\n",
    "        df.loc[0 if pd.isnull(df.index.max()) else df.index.max() + 1] = [recall, d, s0_time]\n",
    "\n",
    "df.sort_values(by=[\"difficulty\", \"retention\"], inplace=True)\n",
    "df.to_csv(\"./expected_time.csv\", index=False)\n",
    "print(\"expected_time.csv saved.\")\n",
    "\n",
    "optimal_retention_list = np.zeros(d_max)\n",
    "for d in range(1, d_range+1):\n",
    "    retention = df[df[\"difficulty\"] == d][\"retention\"]\n",
    "    time = df[df[\"difficulty\"] == d][\"time\"]\n",
    "    optimal_retention = retention.iat[time.argmin()]\n",
    "    optimal_retention_list[d-1] = optimal_retention\n",
    "    plt.plot(retention, time, label=f\"d={d}, r={optimal_retention}\")\n",
    "print(f\"\\n-----suggested retention (experimental): {np.inner(difficulty_distribution_padding, optimal_retention_list):.2f}-----\")\n",
    "plt.ylabel(\"expected time (second)\")\n",
    "plt.xlabel(\"retention\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Evaluate the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Loss\n",
    "\n",
    "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
    "\n",
    "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss before training: 0.3308\n",
      "Loss after training: 0.3118\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = forgetting_curve(dataset['delta_t'], dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss before training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = forgetting_curve(dataset['delta_t'], dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss after training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calibration graph\n",
    "\n",
    "1. FSRS predicts the stability and retention for each review.\n",
    "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
    "3. Count the true retention of each bin.\n",
    "4. Plot (predicted retention, true retention) in the line graph.\n",
    "5. Plot (predicted retention, size of bin) in the bar graph.\n",
    "6. Combine these graphs to create the calibration graph.\n",
    "\n",
    "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
    "\n",
    "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
    "\n",
    "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9363\n",
      "RMSE: 0.0172\n",
      "[0.13218264 0.85228564]\n",
      "Last rating: 1\n",
      "R-squared: 0.3741\n",
      "RMSE: 0.0600\n",
      "[0.31633398 0.61599378]\n",
      "Last rating: 2\n",
      "R-squared: 0.5811\n",
      "RMSE: 0.0392\n",
      "[-0.15088524  1.12631012]\n",
      "Last rating: 3\n",
      "R-squared: 0.9367\n",
      "RMSE: 0.0185\n",
      "[0.06782225 0.93575108]\n",
      "Last rating: 4\n",
      "R-squared: 0.7115\n",
      "RMSE: 0.0251\n",
      "[0.26688374 0.72653442]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NmpTt883qfMx89dVX1K1bl9OnTzNlyhRu3rxJ37596dOnD+fOneOPP/7gwIEDjB07NsvrYA1RUVH8/vvvABbr382bN+ncuTMvvfRSumsCDB48mEePHrFnzx7++ecffvjhB0JCQtLMP336dHr37s358+d57bXX2L9/P4MHD+btt9/m0qVLLFmyhBUrVlg+z//88w/ffPMNS5Ys4fr166xbt47atWvn6FyHDh3KiRMn2LBhA4cPH0aSJLp06ZKq1WR8fDxfffUVq1atYt++fdy7dy9H729RJTRGwzfbr1J/9ia++u8Jem0JihHCGu8veE/5K1EJBsLLdSei71rCncoQHh6e7p8xn1RSKKoIF3AeYK4DWLqYK2Gx2qJrAdRmbQGM18XjPsc9r7aUitgPYnFTZ1yexyz2VAo5jZq35IvpH5Ko1aHXajh9+jStW7dGp9OxePFiwGTB0Wg0tG3bFo1Gw6effsqOHTto1szU5qhChQocOHCAJUuW0Lp16zTrff/997z44ouWH6YqVapw6NAhNm7caBljMEoYjUbmzv+BauX8UfiWpVufV9i5cxcAXl5eqNVqXF1dU7mX58+fT7169fj0008tz/3000+UK1eOa9euUaZMGX766SdWr15N+/YmN/fPP/9MmTJlcnRtwWRdOnbsGCEhITg5OQEm4bNu3Tr+/vtvRo4cSd26dalbt67lmFmzZrF27Vo2bNjA2LFjuXfvHm5ubnTr1g0PDw/KlStHvXr1Mj3XZyldujTfffcdkydPZsaMGTRs2JC2bdsyYMAAKlSoAMCvv/6K0Wjkp59+wtnZmZo1a/LgwQPefPPNbJ1zVudjpl27drzzzjuWx6+//jovv/wyb7/9NnK5nMqVK/Pdd9/RunVrFi1alOl1yAzz+2e26vXo0YNq1aoBphuUAQMGWKxpz655584dduzYwfHjx2nY0FSnbenSpVSuXDnNOv379091M/Haa6/x/vvvM2TIEMD02Z81axaTJ09m2rRp3Lt3jxIlStChQwdUKhVly5alcePGANk61+vXr7NhwwYOHjxI8+am5KhffvmFwMBA1q1bR9++fQEs/07NoRljx45l5syZWV6/os6jyATm777BXyfuJ9VBVaCXhdK52E4WcRhVfCjhOifm+k3F2bMxHHoCPEl3rsS4GCZ2qydc+A5ECMA8wBwDGFjMhbP3I3kaq8FglPJFH9m85Nk6gAUNswtYIZPRpHlLEuLjOHr0GPGx0VSpUgU/Pz9at27NsGHDSExMZM+ePVSoUIGyZcty8eJF4uPj6dixY6o5tVpthj9mV69epXfv3qmea9y4cWoBKEmUCixLsWKeyGUy5DIZxf1LEBKa1iqTkrNnz7J7927c3dOK7Zs3b6LRaNBqtTRp0sTyvI+PD1WrVs38ImWxZmxsbJquLwkJCZYkmdjYWKZPn86mTZt4/Pgxer2ehIQEi8WsY8eOlCtXjgoVKtC5c2c6d+5M7969s11MfMyYMQwePJg9e/Zw5MgR/vrrLz799FM2bNhAx44duXz5MnXq1EnVUcUs3LNDVudjxiyozJw7d45z586lChGQJJPYv337do6vw/79+3F1deXIkSN8+umnlpsVML0/586d45dffkl3zWvXrqFUKqlfv77l9UqVKlGsWLE06zx7PmfPnuXgwYOpLNgGg4HExETi4+Pp27cv8+bNs5xPly5d6N69O0qlMlvnevnyZZRKZarPra+vL1WrVuXy5cuW51xdXVPF5ZYsWTJdS6bAxJPoRBbsvsHvx+5b6tomyi+jVW1iQ2VvWtzcg0wyQvEq0OFbnC8qcfP0duymBVkiBGAeYI4BLOnljFxmciU+jdPg75E3/WvzCwnarJNAXFWuxH4Qa9V8kiRx4VEUANVLeKJU5C6iwVWV+Y+nJeFCBhUrVSKgZCl279lNfEy0xYJXqlQpAgMDOXToELt376Zdu3aASQgAbNq0idKlS6ea12wNywlGo4RKqUSddO4KuSn2LSvXSmxsLN27d+fzzz9PMZep/2zlypW5detWjveU2ZolS5Zkz549aV4zx9JNmjSJ7du389VXX1GpUiVcXFx4+eWX0WpNGdceHh6cOnWKPXv2sG3bNqZOncr06dM5fvx4trOPPTw86N69O927d2f27Nl06tSJ2bNnpxHpGSGXy9OEDaR0M1pzPmbc3FJbnmNjYxk6dCjvvPNOmlZwZcuWRa1W5+g6lC9fHm9vb6pWrUpISAj9+vVj3759ljXfeOONVLF3Kde8du1altcks/OZMWMGffr0STPW2dmZwMBArl69yo4dO9i+fTujR4/myy+/ZO/evTZ9z808m5Qik8lEoed0CIlJZPGeW6w+etfyO5YoP0ek8hdalJCxXh2A+w2Tt4G6/4MuX0GcFi7ecOCuBdYiBGAeYI4BdFEr8XFzIixWQ2hMERaAmVgAZTJZpm7YZ3FTG5AkCReVK2qlfS2LZgugXCZDKZfRqHlL9u3dS2x0FO+++65lXKtWrfjvv/84duyYxWVYo0YNnJycuHfvXrru3vSoWrUqx48fT/Xcs4/NJV9UKQQgACl+y9RqdZpYt/r16/PPP/8QFBRkyTg1Go1ER0fj5uZGxYoVUalUHD16lLJlywKm4Ppr165Zvf9nqV+/PsHBwSiVSoKCgtIdc/DgQYYOHWqxfMbGxqZJ5FAqlXTo0IEOHTowbdo0vL292bVrF3369En3XK1BJpNRrVo1Dh06BED16tVZtWpVqozulMk9YIoljImJIS4uziJ4zAki2Tmf9KhXrx5Xr16lUqVKGfYCzuw6WMOYMWOYM2cOa9eupXfv3tSvX59Lly5RqVKldMdXrVoVvV7P6dOnadCgAQA3btwgIiIiy7Xq169vOZ+McHFxsQjyMWPGUK1aNc6fP0/9+vWtPtfq1auj1+s5evSoxQX89OlTrl69So0aNay6LgJ4Gqthyb5brDx8h0RzK1PVTYJlS9EqLrCkZn+G3zmKLOQ4qFxNwq/eANO4uHDHbVyQLYQAzAPMLmAnpRw/j2QBWNSwdSFoMNUCNEh5kwlsNhDIZaBUyGnUrCWfTXkXnU6XShS1bt2asWPHotVqLQkgHh4eTJo0iQkTJmA0Gnn++eeJiori4MGDeHp6WmKjUvLWW2/RqlUr5s6dS/fu3dm1axf//fdfqixTc8kXtTJJAJqLY6eYJygoiKNHj3Lnzh3c3d3x8fFhzJgx/Pjjj/zvf/9j8uTJ+Pj4cO3aNVavXs2KFStwd3fn9ddf591338XX1xd/f38++uijDMVISgwGQxoh5OTkRIcOHWjWrBm9evXiiy++oEqVKjx69IhNmzbRu3dvGjZsSOXKlVmzZg3du3dHJpMxZcqUVNbMjRs3cuvWLVq1akWxYsXYvHkzRqPR4ppO71yf3fOZM2eYNm0agwYNokaNGqjVavbu3cuyZct47733AFMM20cffcSIESP44IMPuHPnDl999VWqeZo0aYKrqysffvgh48aN4+jRo6xYsSLVmKzOJyMmT55M8+bNeeuttxgxYgRubm5cunSJ7du3M3/+/CyvgzW4uroyYsQIpk2bRq9evXjvvfdo2rQpY8eOZfjw4WnWrFatGh06dGDkyJEsWrQIlUrFO++8g4uLS5a13KZOnUq3bt0oW7YsL7/8MnK5nLNnz3LhwgVmz57NihUrMBgMlmu6evVqXFxcKFeuXLbOtXLlyvTs2ZMRI0awZMkSPDw8eP/99yldujQ9e/a0+toUVSLitPy4/xYrDt2xJO35ekZzOfFLEuSnqeBdjl1BIyl35ndAAr/q0HcF+Fdz6L4FOUNkAecBZgugk1KOv4fJ3VfUikFr9UZL5wxXle3uO5L7AdtfAZrXkKWwACYkJFCpUiUCAgIs41q3bk1MTAxVq1alZMmSludnzZrFlClTmDNnDtWrV6dz585s2rSJ8uXLp7teixYtWLx4MXPnzqVu3bps2bKFCRMmWCxSBqPRIkrTWABTSMBJkyahUCioUaMGfn5+3Lt3j1KlSnHw4EEMBgMvvPACtWvXZuLEiXh5eVkE05dffknLli3p3r07HTp04Pnnn7dYfjIjNjaWevXqpfozC6DNmzfTqlUrhg0bRpUqVXj11Ve5e/eu5frNnTuXYsWK0bx5c7p3706nTp1SxZx5e3uzZs0a2rVrR/Xq1Vm8eDG//fYbNWvWzPBcn6VMmTIEBQUxY8YMmjRpQv369fn222+ZMWMGH330EQDu7u78+++/nD9/nnr16vHRRx+lcpeDKSZy9erVbN68mdq1a/Pbb78xffr0VGOyOp+MqFOnDhs3buTatWu0bNmSevXqMXXqVEqVKmXVdbCWsWPHcvnyZf766y/q1KnD3r17M1wTYOXKlQQEBNCqVSt69+7NiBEj8PDwSBUrmR6dOnVi48aNbNu2jUaNGtG0aVO++eYbypUrZzmfH3/8kRYtWlCnTh127NjBv//+i6+vb7bPdfny5TRo0IBu3brRrFkzJEli8+bNdq1FWNCJitcxd9tVWn6xm4V7bhKvNVA5QI17wM+c0vYnQXGa8bX6cdWzGuXO/AZIUG8QjNglxF8BRiYVscCHBw8eEBgYyO3btzN0Q9maUatOsuViMLN61uTsgyj+PvmAdztVZUzbjN0hhY2oBB11Z2wD4OrszjgpFcTExHDt2jWqV6+e7SB+M1eDY9DoDVTwc8fdyb4G7YeRCTyNNbnu5XIIjkqkmKuaQJ+c7T0njBgxgitXrrB//34SdQauPYlBIZdRs5Spe8j98Hgi4rWU8HLOdoiB2QXs6elplaWvqHHnzh3Kly/P6dOnee655+y6VkF5L8zfpzt27LBkixc24uPjuXz5MlWqVMHDw8PR27EpeoORnw7cZv7uG8QkmmoyVi/pQeWyV/nhwltojBp8XXxZ12gczx//GeLDQOUG3edBnVfSnTM8PJyFu29kmQQSFx3J6LaVspUFbP43eP/+/VxVJBCYEC7gPMCcNeWkVOCXZAEsai5gc/yfQi6zJCzYguR2cPa/j5GM5hhAUCb9KOsM9q1j9dVXX9GxY0fc3Nz477//+Pnnn1m4cCGQnFyU8nqaLYD27AcsKLrs2rWL2NhYateuzePHj5k8eTJBQUHZqkEoyB9cehTN5H/OcuFhNABVAzwY3MKH5Vcm8P05U7HyrhU787tvbdz3zgUkCKhtcvkWLzrGi8KMEIB5gCUGUJXsAi5yAjBFCRhb9n40u4ClPBA8yVnAMlQK07p6O6977NgxvvjiC2JiYqhQoQLfffcdw4cPB5LFp0oIQEEeodPp+PDDD7l16xYeHh40b96cX375RbhXCxAavYH5u26waM9N9EYJLxcVH3etjt7pEKM39yQyMRIXpQuLW05l0I09yI4uMR3Y8DXo9CmoXBx7AgKbIQRgHmAuBG1OAoGiJwDjtSb3gi0TQCBv28ElZwGDMmldvcG+C//5558Zvma2LJsTQEAIQHsSFBRU5EuFdOrUiU6dOjl6G4IccupeBJP/PseNEFNZqhdrleCdToFM2z+BX8//CkCjUo1YU28UZXbOgoRwUHtAj++glnXZ5YKCgxCAeUByEoiCYkkCqKh1A0m0UxHovHQBpyoDk2R1MyViSDa1alqLTp+6BAwIASgQCNISr9Xz1dZrLD90G0mC4u5OzOpZEyf3y7Rd3YsH0Q9QyBRMafE+H+slFBsnmA4sWRdeXg6+FTNfQFAgEQIwD7DEainl+HuaAvOLmgUwQWu6Bpm1gcsJyVnANp02XVKVgZEnl1vRGyWLSzgvsVgAU6xtLgMjBKBAIAA4dCOM99ec5154PAB96pdmcueKfHF4Gt8c+QaAisUq8mfHr6l/8Ht4kFRrtPEb8MIsUOa8UL0gfyMEYB7wbB1AgFiNnnitHld10XgLzC5gZxtbAGUOsADKkrptKOVy9EZjkgC0+/JpsMQApucCLuKuSoGgqBOdqOPTTZf5/fh9AEp5OfNJn9r4eD2mw+pmXAy9CMDI+iOZV/4FXNaPg8RIcPKCnvOhRg8H7l6QF+Tf+gKFiJQuYDe1wuIGLUpWQHMSiP0sgHmXBCLHtKbSnAhi50zg9PciiSQQgUCQLjsuPaHj3L0W8TeoaTn+G/88x8NW0OjHRlwMvYi/mz8bX1nLEmUxXP5+zST+StWHUfuE+CsiFA3zk4OxCECVHJlMhr+nE3efxhMao6Gcr/Vtzwoy1rSBywl56wJOigFM0luWRBAHiC2z+DMXpTaTUgA6KjZRIBA4hqh4HVPWX2DD2UcAlC/uxmd9ahPgE0P33zuy/95+AHpU7cFPLadQfNMkeHTadHCzsdB+GijVjtq+II8RAjAP0OiSXcAAfu4mAViUuoHYow0cJIsxYx6WgTGLTnMtQHtnAqeHOQFErZCnEnmKFGLQIEkohQAUCIoE5x5EMvqXUzyISEAugxGtKjC+fWX+vPQLHf94ixhtDO5qd77t/C3D1D7IVvYGTTQ4e0PvxVD1RUefgiCPES7gPODZch1FsRRMvJ0tgHlRniO5FZzpscUFbEVvV1ujtbh/Uws8uUyGXCajbmAx1q5dZ/d9rFixAm9vb8vj6dOnp+qSMXToUHr16mX3faQkKCiIefPm5emaAoGjkCSJVUfu8vKiwzyISKCsjytrRrdgRGs/Bq17laHrhxKjjaF5YHPODT/Ga48uIftriEn8BTaBUQeE+CuiCAFoZwxGCV2ShchJaRI/RbEYdKK9LICWJBCbTpsGSZKSXcCy5BjAsyePUcbHna5du2Z7ztwIFZ0hbRcQM2YrYFZW0eDgYN566y0qVKiAk5MT5cqV49VXX2Xnzp052hOYevHm5vjs8Kz4NHP8+HFGjhyZJ3sQCBxJnEbP27+fYcq6C2gNRl6oEcC/bz1PsOYItRfV5p/L/6CUK/mk3Sfs676c8v+MhOM/mg5uMR6GbgLvQIeeg8BxCBewnTGXgIEULuAkARgSk+iQPTkCiwWwgCaBSEl/pjVN/1XK5az9fTWDh4/i719X8ujRI0qVKmXXfZjR6dNmAJuxCMBMrsmdO3do0aIF3t7efPnll9SuXRuNRsOGDRt46623uHLlSo725e7ujru7e46ONaPValGrcx6H5Ofnl6v1BYKCwLUnMby5+iQ3Q+NQyGW837ka/ZsGMHn7eBaeMLWLrF68Oqv7rKZ+2C34sR1oY8DVF3ovgcodHXwGAkcjLIB2xlwCBtIKwKJkAUywWyHovEkCSSmmzDF3iQlxbP13La8Ofp2uXbuyYsWKNMf9+++/NGrUCGdnZ4oXL07v3r0BaNOmDXfv3mXChAnIZDLLnM+6UAHmzZtHUFCQ5fHx48d5tXc3WtepSOXAAFq3bs2pU6csryusEMWjR49GJpNx7NgxXnrpJapUqULNmjUZM2YMhw4dsoybO3cutWvXxs3NjcDAQEaPHk1sbGyG86a3f4AZM2bg5+eHp6cno0aNQqvVWl5r06YNY8eOZfz48RQvXtzSaSKztffs2cOwYcOIioqyXL/p06cDaS2r9+7do2fPnri7u+Pp6ckrr7zCkydP0ux51apVBAUF4eXlxauvvkpMTEyG5ykQOJI1px7Qc/5BbobGUcLTmT9GNuW5ik+p/0N9i/h7q/FbnBx2gPonfoZ/XjeJv7LNTS5fIf4ECAFod8wWQLkMS/cIf4+kYtBFqBuIOQs4qzIwkiQRr9Vb/Zeg05OoMxCXjWMy+sssjtD8kgwZ5qi7DWv+pnzFypStUImBAweybNmyVHNs2rSJ3r1706VLF06fPs3OnTtp3LgxAGvWrKFMmTLMnDmTx48f8/jxY6uvZUxMDD37/o8Va/5j194DVK5cmS5dulgEiyKL9njh4eFs2bKFMWPG4OaWNgs9pVtVLpfz3XffcfHiRX7++Wd27drF5MmTrd4rwM6dO7l8+TJ79uzht99+Y82aNcyYMSPVmJ9//hm1Ws3BgwdZvHhxlms3b96cefPm4enpabl+kyZNSrO20WikZ8+ehIeHs3fvXrZv386tW7fo169fqnE3b95k3bp1bNy4kY0bN7J3714+++yzbJ2nQGBvEnUGPlhznol/niVBZ6Bl5eKsH9uM/+4uoPmy5lx7eo1SHqXYOnAr3zUci8uKbnByBSCDVu/CkH/BM2+8FIL8j3AB25mUNQDNWFzA0UVPAGZlAUzQGagxdWtebCkNl2Z2yrAwd8oEELO1buWKFXTt8wp6g0SnTp2Iiopi7969tGnTBoBPPvmEV199NZXYqVu3LgA+Pj4oFAo8PDwoUaJEtvbZtm1b/Ko2QJIkqpbw4IcffsDb25u9e/fSrVu3LItB37hxA0mSqFatWpZrjR8/3vL/QUFBzJ49m1GjRrFw4UKr96tWq1m2bBmurq7UrFmTmTNn8u677zJr1izkSZnUlStX5osvvrB6bbVajZeXFzKZLNPrt3PnTs6fP8/t27cJDDTFOq1cuZKaNWty/PhxGjVqBJiE4ooVK/Dw8ABg0KBB7Ny5k08++cTq8xQI7Mndp3GM/uUUFx9FI5PBuHaV6VJPRq+/2nPkwREAXqn5Cou6LsLn2jb4fSjo4sDND/r8ABXbOfYEBPkOYQG0M5YuIKrkS20WgE/jtEWmYG9yGZiCec/xbAmYq1evcvz4MTr3fAkJCZlcQb9+/fjpp58sx5w5c4b27dvbfC8PHwcz/d1xdG/ZAH9fHzw9PYmNjeXevXtA1kkg2cmY3rFjB+3bt6d06dJ4eHgwaNAgnj59Snx8vNVz1K1bF1dXV8vjZs2aERsby/379y3PNWjQwC5rX758mcDAQIv4A6hRowbe3t5cvnzZ8lxQUJBF/AGULFmSkJAQq9cRCOzJlgvBdPv+ABcfRePjpmbF0Ea4++ylwQ/1OPLgCF5OXqzuvZrfu/+Ez9YpsPYNk/gLamly+QrxJ0iHgvlrXIBI1JktgMkC0NdNjUxmyhCOiNdS3L3w91q01gLoolJwaWYnq+fV6AxcD4lFIZNRvZRnrvaY2d4kozkD2PT4p59+Qq/X07Fh9eQxkoSTkxPz58/Hy8sLFxeXbO9BLpenEWg6nS7V42FDh/L4SSgfzPqclvWq4+TkRLNmzSxxdVm5gCtXroxMJssy0ePOnTt069aNN998k08++QQfHx8OHDjA66+/jlarTSXqcsuzrui8XBtApVKleiyTyTA6oLyPQJASncHI5/9dYemB2wA0KFeMaT0D+XjvcP699i8AbYLa8HOvnymriYOl7SH0Csjk0Pp9aDUJ5A7oUykoEAgBaGfScwErFXJ83dSExWoJidYUDQFoZSs4mUyWrf7IKoUcZ5UCGSYBZ6/OF2YxJZPJ0Ov1rFy5kq+//prK9Vug1RsILOaCq5OKXr168dtvvzFq1Cjq1KnDzp07GTZsWLpzqtVqDAZDquf8/PwIDg5O1cXjzJkzqcYcOXyI92d/SYcXOlPJ35379+8TFhZmeT2rLGAfHx86derEggULGDduXBrxFRkZiY+PDydPnsRoNPL1119bXLV//vmndRcsBWfPniUhIcEiiI8cOYK7u3sqq9yzWLN2etfvWapXr879+/e5f/++Zb1Lly4RGRlJjRo1sn0uAkFe8SgygXG/nebE3QgARrQsT40KN+jwSy9C40NRK9R82u5TJjQdj/zMr7D5XdAngHsJeGkplG/p4DMQ5HeEC9jOmJNA1M+U6zCLvqKSCBKv1QPgbPMsYNN/JZITNeyBUUq2AG7cuJGIiAhef/11atasSeVqNahSvSa1atXipZdesriBp02bxm+//ca0adO4fPky58+f5/PPP7fMGRQUxL59+3j48KFFwLVp04bQ0FC++OILbt68yYIFC/jvv/9S7aV8xUps/OdP7t68xtGjRxkwYEAqa6MlCziT8IIFCxZgMBho3Lgx//zzD9evX+fy5cssWbKEFi1aAFCpUiV0Oh3ff/89t27dYtWqVZYEjeyg1Wp5/fXXuXTpEps3b2batGmMHTvWIuzSw5q1g4KCiI2NZefOnYSFhaXrGu7QoQO1a9dmwIABnDp1imPHjjF48GBat25Nw4YNs30uAkFesPtKCF2/28+JuxF4OCmZ92pN7hi+oc+fPQmND6W2f21OjDjBOw3eQL7uTdgw1iT+KrYzuXyF+BNYgRCAdsYSA/iMACxqpWDMrvCsLIDZRZ7C4mfPWoApi0D/9NNPdOjQAS8vL0s7OHOx75deeokTJ05w7tw52rRpw19//cWGDRt47rnnaNeuHceOHbPMOXPmTO7cuUPFihUtteuqV6/OwoULWbBgAXXr1uXYsWNpslu/+m4hMVGRdGvXgkGDBjFu3Dj8/f0tr2flAgaoUKECp06dom3btrzzzjvUqlWLTp06sXfvXhYsWACYYvfmzp3L559/Tq1atfjll1+YM2dOtq9d+/btqVy5Mq1ataJfv3706NHDUrIlI6xZu3nz5owaNYp+/frh5+eXJokETBbb9evXU6xYMVq1akWHDh2oUKECf/zxR7bPQyCwNzqDkTmbLzNsxXEi4nXUKu3JrL5uvLOvPUtPL0WGjEnNJnF8xHFqSzL4oTWc+wNkCmg/FQb8A+6iDqbAOmRSXvTQykc8ePCAwMBAbt++naq2mr3YejGYN1adpH5Zb9aMbmF5/p0/z/LPqQdM7lyV0W0q2X0fjqbezG1ExOvYNqEVVQJMwfYxMTFcu3aN6tWr5yqm6/zDKFNWawnPNJZWWxERp+V+RDzuTkoq+CUXOn4UmUBYrAY/DydKemU/5i8n3AmLIzpRR2lvF3zTCR+I0+i5GRqLWimnWgnr4yKNRiPR0dF4enpmap0T2B/xXuQf4uPjuXz5MlWqVEmVKGRrHkYm8Navpzh1LxKAQc3KYnD7i88PfYJRMlLWqyw/9/qZNuVaw8nl8N/7YNCARyl4eRmUa2a3vWWH8PBwFu6+gZund6bj4qIjGd22Ej4+PlbPfefOHcqXL8/9+/cpU6ZMLncqEDGAdia9GEAoehZAexWCBpNb1iDZ1wJofKYNnBllkrVNb8i7+yidpQ9w+sLAUgamiGSYCwQFnR2XnjDp77NExuvwcFby9gu+LL4wjJOPTwIwqM4gvn/xe7yQwd+vwcU1pgMrvwC9FoObrwN3LyioCAFoZzRJwudZy1RREoBGo2RxAdu6FRyYRJkBKVvlTbKLpQyM/BkBmCTC9HkotrSG9ONKzaQsA5MymUQgEOQvdAYjX2y5wo/7TVm+dcp40bjmBd7a3YtEfSLFnIuxpNsS+tbsC4/OwN/DIPwWyJXQfho0GwvCQizIIUIA2hnzj/WzMYD+ln7AhV8AJqZoh2cfC6D928FZYgCfeT7ZApg3JUMMRsli2VMp0hd25iQQCZPlUiEEoECQ73gQEc9bv53mdJLLt19jP07HTmPKvi0AvFDxBZb3XE4p95Jw9AfY9hEYtOAVaHL5BjZ24O4FhQEhAO2MxlwHUJW+CzisCAjAeK29BaDpv/Z1ASetlcYCmCQA88gCaHb/KuQyFBnc+cvlMuQyGUbJJBYz8BQLBAIHsf3SEyb9dZaoBB2ezkr6NI3j+3MvEp4QjrPSmS87fsmYRmOQJUbBn4PgsqnmH1W7QM8F4Gp93JxAkBFCANqZ5BjAousCNheBdlLK0wgoWyCTZV73zhakbAWXEnMWsN6QN+5Wc1mhjOL/zCjkMowGScQBCgT5CK3eyOdbrvBTUmHnWqXdcfX7jRlHlwDQoGQDVvdZTbXi1eDBSfh7KETeA7kKXpgFTUal/RISCHKIEIB2JqMyMGYXcIxGT4LWYJfYuPyCtUWgc0qyBdAu0wOpy8CkxGwBlDCJLWUGbllbYbYAqrMSgDIZOkQiiECQXwiN0TBi5QnO3I8EoHMdFf89Gca9q7eRy+R8+PyHTG09FZVcCYcXwPZpYNSBdznouxxKp22XKBDkBiEA7UxGhaDdnZQ4q+Qk6oyExmgo62vb1lb5CWvbwOUUuRWFj3NLci/gtGsr5DIMRgm9UUJpZx1vjilVZVHuRmQCCwT5h8dRCQxYepRboXF4uSipVek0P1z/EAmJisUqsqr3KpoFNoP4cFg/Bq5uNh1YvQf0+B5cvB26f0HhRAhAO5NRGRiZTIafhxP3wxMIjU0s3ALQXALGXhZAKwof55ZkF3BaC59SLsdgNJgSQewkcs3o9KZ9qLOwNFoEYNEq8ykQ5Dvuh8fTf+kR7ocnUNxDQYLHZ/x6Yw8Aw+sN55vO3+Cudof7x+CvYRD9ABRq6PQpNBouXL4CuyEEoJ3JyAUM4O/hbBKAhTwO0GIBtLsL2J6dQMxrpSMAFTI0+rxJBMmqBqAZYQEUCBzPrdBYBiw9yuOoRLxctVwyvEVCxEP8XP1Y2mMpPar2AKMRDsyDnTNBMoBPBei7AkrWdfT2BYUckR9oZ5KzgNNeaj/3olEKxhIDqLLP/YY8D5NA0sthsXUx6OnTpxMQEIBMJmPdunWpXtNaGwPoQAG4Z88eZDIZkZGRAKxYsQJvb2/L69OnT+e5557L0z21adOG8ePH5+magqLN1eAYXllyhMdRiajUT7loHE6C9JDuVbpzYfQFk/iLewq/vgI7ppnEX62XYOReIf5sxJw5c2jUqBEeHh74+/vTq1cvrl69mmpMmzZtkMlkqf5GjRqVasy9e/fo2rUrrq6u+Pv78+6776LX61ON2bNnD/Xr18fJyYlKlSqxYsWKNPtZsGABQUFBODs706RJk1StQR2BEIB2JiMXMBSdTGBzGRhnO1sA7entNOsoswt46NChli+LyiWL0e35+nwxZ3aaL4XscvnyZWbMmMGSJUt4/PgxL774Yoo9SJZ6g9mJAcyO4IqOjubjjz+mWrVqODs7U6JECTp06MCaNWtyXGi7X79+XLt2LUfHZpdnxaeZNWvWMGvWrDzZg0Bw4WEUr/5wmLBYDXr5HW7J38JZreHH7j+y/tX1+Lv5w91DsPh5uLEdlM7Q/Vt46Sdwtr59oyBz9u7dy5gxYzhy5Ajbt29Hp9PxwgsvEBcXl2rciBEjePz4seUvZV9xg8FA165d0Wq1HDp0iJ9//pkVK1YwdepUy5jbt2/TtWtX2rZty5kzZxg/fjzDhw9n69atljF//PEHEydOZNq0aZw6dYq6devSqVMnQkJC7H8hMkC4gO1MRkkgkJwJXNgFYLIFsCAngaS1AHbu3Jnly5fzMCyKtRs28unH71LMw5UPPvgg2/MbDAZkMhk3b94EoGfPnmniDfUGIxImEarMopyOufhzdiyAkZGRdOrUidjYWGbPnk2jRo1QKpXs3buXyZMn065du1SWPGtxcXHBxSV3fZK1Wi1qtTrHx2en36hAkBtO3o1gyLKjxGoMaGRXCVFPpXFgbVb1XkUln0oml+/+r2D3pyAZwbeyyeVbopajt17o2LJlS6rHK1aswN/fn5MnT9KqVSvL866urpQoUSLdObZt28alS5fYsWMHAQEBPPfcc8yaNYv33nuP6dOno1arWbx4MeXLl+frr78GoHr16hw4cIBvvvmGTp06ATB37lxGjBjBsGHDAFi8eDGbNm1i2bJlvP/++/Y4/SwRFkA7k1kMoF8R6QaSoDVZxewVA5hcB9Au0wPpl4FxcnKiRIkSlC8fxCuDX6dF67Zs2LABAI1Gw6RJkyhdujRubm40adKEPXv2WI41u0U3bNhAjRo1cHJy4rXXXqN79+6mdeTyVAJw6dKl1K5Vi0aVStCzdSMWLVqUan8PHjzgf//7Hz4+Pri5udGxdXPOnT7B77+uYsaMGZw9e9ZisUzPNQHw0Ucfcf/+fQ4fPsyQIUOoUaMGVapUYcSIEZw5cwZ3d3cAVq1aRcOGDfHw8KBEiRL0798/07vYZ13AZpYsWUJgYCCurq688sorREVFWV4bOnQovXr14pNPPqFUqVJUrVo1y7Xv3LlD27ZtAShWrBgymYyhQ4cCaV3AERERDB48mGLFiuHq6sqLL77I9evX0+x569atVK9eHXd3dzp37szjx48zPE+B4NDNMPr/eIhYjYFE+QWeukxnRrv32T9sv0n8xYbA6j6wa7ZJ/NV5FUbuEeIvm8TExBAdHW3502is+w01f8c8e0P4yy+/ULx4cWrVqsUHH3xAfHy85bXDhw9Tu3ZtAgICLM916tSJ6OhoLl68aBnToUOHVHN26tSJw4cPA6Yb2JMnT6YaI5fL6dChg2WMIxAWQDuTUSFoKDou4ARtNvoASxLo4rMelwK5XoNMl4ik0IE2FypQ5Zphxl1GZWAguRi0k5Mz8dGRAIwdO5ZLly7x+++/U6pUKdauXUvnzp05f/48lStXBiA+Pp7PP/+cpUuX4uvrS8mSJWnTpg3Dhg1LJTR++eUXpk6dymdfzaN4UFXuXr3Ax5PG4ebmxpAhQ4iNjaV169aULl2aDRs2UKJECQ4cOYZkNNKlRx9C7t5gy5Yt7NixAwAvL6+052c08scff/Dyyy9TqlSpNK+bxR+ATqdj1qxZVK1alZCQECZOnMjQoUPZvHlz1tc4iRs3bvDnn3/y77//Eh0dzeuvv87o0aP55ZdfLGN27tyJp6cn27dvt2rtwMBA/vnnH1566SWuXr2Kp6dnhpbHoUOHcv36dTZs2ICnpyfvvfceXbp04dKlS6hUKsv789VXX7Fq1SrkcjkDBw5k0qRJqfYoEJjZdukBo1afwmhUkCA/hXeJP9n48i4almpoGnBrL6wZAbFPQOkCXb+C5waILN8cUKNGjVSPp02bxvTp0zM9xmg0Mn78eFq0aEGtWsmCu3///pQrV45SpUpx7tw53nvvPa5evcqaNWsACA4OTiX+AMvj4ODgTMdER0eTkJBAREQEBoMh3TFXrlyx/sRtjBCAdiazGEB/D2eg8AvAeF2SBdAaF7AuHj5NK0AywzfpL9d8+AjUbum+lFkZGIUcjuzfw/49Oxn31lvcu3eP5cuXc+/ePYuYmjRpElu2bGH58uV8+umngEnMLFy4kLp1kwO+zZaylO6IadOm8fXXX9Ouaw+eRCdSu1plnj68zZIlSxgyZAi//voroaGhHD9+3HJnWzIwiJuhsaiUctzd3VEqlRm6OADCwsKIiIigSpUqWV6m1157zfL/FSpU4LvvvqNRo0bExsamEoqZkZiYyMqVKyldujQA33//PV27duXrr7+27NPNzY2lS5emcv1mtbb5/P39/TN0V5uF38GDB2nevDlgEtmBgYGsW7eOvn37Aqb3Z/HixVSsWBEwifqZM2dadX6CosXC/Yf4YlMooCRefpSXmkfydaejuKpcwWiAvV/A3s8BCfyqQd+fwb+ao7ddYLl06ZLluwNM3pisGDNmDBcuXODAgQOpnh85cqTl/2vXrk3JkiVp3749N2/etPzbL6wIAWhntFZYAMNiNRiNkl3apOUHEu1cCDovkNKxAG7cuBF3d3d0Oh1Go5EXe73MtGnT2Lt3LwaDIY2Y0mg0+PomS1W1Wk2dOnUyXTcuLo6bN2/y+uuvI5OPQJJMBgODXm+x5J05c4Z69eqlcmtkNws4OwkeJ0+eZPr06Zw9e5aIiAiMRtNn/N69e2nuzDOibNmyqb7AmzVrhtFo5OrVqxYBWLt27TRxf7ZY+/LlyyiVSpo0aWJ5ztfXl6pVq3L58mXLc66urql+AEqWLOnQgG1B/sNgNDDizyXsPFMGGUoMTidYPqAZXaskJW/FBMM/w+HOftPjegPhxS9BXXjrvuYFHh4eeHpanywzduxYNm7cyL59+yhTpkymY83fCzdu3KBixYqUKFEiTbbukydPgOQb9RIlSlieSznG7IVQKBQoFIp0x2R2Y25vhAC0M5nFAPq6m37c9EaJiHgtvu5Z38UUROKzUwdQ5WqyxGWDmEQdd57G46xSUNnfOgtUhmungyRJKZJAkhVg27ZtWbRoEUqlinDcUCqVOLu4Ehsbi0Kh4OTJkygUqc85pYXMxcUly97BsbGxAPz444+UrFSLeK2BEl7OeLuqLXOn5+ZMFoDWiTs/Pz+8vb2zzNaNi4ujU6dOdOrUiV9++QU/Pz/u3btHp06d0Gq1Wa6THdzcUltj83JtwOIKNiOTyXKcCS0ofNyKuEWfFV8R8eRFZMjx9bnB5tFjCHAvbhpwcxesGQlxoaByg27fQN1+jt10EUOSJN566y3Wrl3Lnj17KF++fJbHnDlzBjDd8IHp5vSTTz4hJCQEf39/ALZv346np6flprNZs2ZpQmC2b99Os2bNANPNfoMGDdi5cye9evUCTC7pnTt3MnbsWFucao4QAtDOWFzA6dQBVCnk+LipCY/TEhqrKbQC0NIJxBoLoEyWoRs2I+SSHkkFRqU828daQ0ojWkrB5ubmRqVKlQCIexRlaQdXr149DAYDISEhtGzZMldrBwQEUKpUKW7dukXD9j3Q6A1UKO6Gu3OyOKlTpw5Lly4lPDzcYgU0ZwFLSKjUagwGQ6bryOVy+vXrx+rVq5k9e3aau+TY2FicnZ25cuUKT58+5bPPPiMwMBCAEydOZPu87t27x6NHjywu8iNHjiCXyy3JHulhzdpmi2Fm51u9enX0ej1Hjx61uICfPn3K1atXrbYiCooukiSx4MhyZm++gLOuKzKgUaVEfh/2FgqFHAx62DMH9n8NSBBQy5TlW7yyg3de9BgzZgy//vor69evx8PDwxKz5+XlhYuLCzdv3uTXX3+lS5cu+Pr6cu7cOSZMmECrVq0s3pkXXniBGjVqMGjQIL744guCg4P5+OOPGTNmjMX1PGrUKObPn8/kyZN57bXX2LVrF3/++SebNm2y7GXixIkMGTKEhg0b0rhxY+bNm0dcXJwlK9gRiCxgO2MpBJ1Bk1hzKZiQ6MIbB5hoLgNj904gdpk+ldUnIy+9ORFEb5SoUqUKAwYMYPDgwaxZs4bbt29z7Ngx5syZk+oLwVpmzJjBnDlzWPHDQu7cusHVyxdZvnw5c+fOBeB///sfJUqUoFevXhw8eJBbt26xdu0azp06DkDZsuW4ffs2Z86cISwsLMOMudmzZ1O6dGmaNWvGypUruXTpEtevX2fZsmXUq1eP2NhYypYti1qt5vvvv+fWrVts2LAhR/X1nJ2dGTJkCGfPnmX//v2MGzeOV155JVN3iDVrlytXDplMxsaNGwkNDbVYUFNSuXJlevbsyYgRIzhw4ABnz55l4MCBlC5dmp49e2b7XARFhyexT2izeBKfr1fjrGsPwP+aFufP1/uYxF/UQ/i5u6nMCxI0GAbDdwjx5yAWLVpEVFQUbdq0oWTJkpa/P/74AzDdMO7YsYMXXniBatWq8c477/DSSy/x77//WuZQKBRs3LgRhUJBs2bNGDhwIIMHD04VD1y+fHk2bdrE9u3bqVu3Ll9//TVLly61lIABUz3Ur776iqlTp/Lcc89x5swZtmzZkiYxJC8RFkA7k5kLGExxgFeCYwp1Iki2XMA5wN6dQFIWgc7IZauUy9CApVDz8uXLmT17Nu+88w4PHz6kePHiNG3alG7dumV7/eHDh6N2dubTz77g60+m4u7mRu3atS1lTdRqNdu2beOdd96hS5cu6PV6atSowTszTMVMe/TqzYb162jbti2RkZEsX77cUh4lJT4+Pmzbto1FixYxe/Zs7t69S7FixahduzZffvklXl5eljIyH374Id999x3169fnq6++okePHtk6p0qVKtGnTx+6dOlCeHg43bp1Y+HChZke4+fnl+XapUuXZsaMGbz//vsMGzaMwYMHp1v2Zvny5bz99tt069YNrVZLq1at2Lx5cxq3r0BgZv2lvXyw8QIKXTsUQDH3RBb3b02TCkku32vbYO0bkBAOag/o8a2ps4fAYWQVshEYGMjevXuznKdcuXJZVjlo06YNp0+fznTM2LFjHeryfRaZVMSCWh48eEBgYCC3b98mKCjI7uvVnLqFOK2Bve+2oZxvWvfkxD/OsOb0Q95/sRqjWhfOjKPeCw9y+l4kPwxqwAs1ky08MTExXLt2jerVq+PqmvOgaJ3eyOXgaGTIqF0mbYmT3JKoM3DtSQwKuYyapdKf/+7TOKISdJTydqG4HVz58Vo9N0JiUSnkVC9pXfDz1eCYdF3GGWE0GomOjsbT0xO5XDgHHIl4L/IPMXGxHD9/jo92hvE4RgHo6N/Ml2ldm5s8OwadqY/voe9MB5SsCy8vB9/C+X2eFeHh4SzcfQM3T+9Mx8VFRzK6baVsFWm/c+cO5cuX5/79+1kmcwiyRlgA7UxmZWAA/DwLvws4wc4WQLNRTsKUrCG3cV2t9IpAP4syqTev2QJoa3RJnyNVFj2AU2JJBCla93gCgc14GhfDg7AYDEZn5Kjx8w5j5dAuVC+RJFoi78Pfr8GDpCzRxiPhhdmgLJzx3ILChRCAdkRvMKJP8h9m6AJOshaFxhZiAWjvGMAUgXn2EICZFYE2Y27NpjfYR2xpk+ZVK6w/N2U2S8EIBAITeoORW0+fkqhVgaQEjPRuoOCdTgOSw0CubIZ1b0JiJDh5Qc/voYaIIRUUHIQAtCPaFNag9LKAIWU3kMQ82ZMjMMcAOtuxF7AMGRISkhGbpzZlVgTajDJJmOntJLZ0SZ8lVQY3EumR3VqAAoEAwmLjeByViCSZwiYUCi1eanijRVPTd4BeCzumwZGkmNVS9aHvcigW5LhNCwQ5wOHBJQsWLCAoKAhnZ2eaNGmSpuDis8ybN4+qVavi4uJCYGAgEyZMIDExf4oncxFoAHUGrjtzN5DC3A/YXAjaVW2/+43kTGDbi53kItAZC0CVJQvYPi5gbW5cwEIACgRZojMYufYknEeROiRJAegp7mmkgq+PpawSEXdgWadk8dd0DLy2VYg/QYHEoRbAP/74g4kTJ7J48WKaNGnCvHnz6NSpE1evXrUUXEzJr7/+yvvvv8+yZcto3rw5165dY+jQochkMktJjPyEOf5PIZdZYsSepbD3A5YkifgM6gCaLWq2yEOSy2UYDJJdBGByEeiMx9jbBWy2AGZ0I5EeciEABQKr0Oq1XAuJxmg0/SQqlXFUKF4cZ6UTcXFxAMhv74FNY0ATBc7e0GsRVOviuE0LBLnEoRbAuXPnMmLECIYNG0aNGjVYvHgxrq6uLFu2LN3xhw4dokWLFvTv35+goCBeeOEF/ve//2VpNXQUyTUAM77MZgEYk6i31MsrTOgMkkWAPJsEolQqMRqNxMfH53odmcUCmOup0pCyDExGmF3AOqNkl24ROXIBy4QAFAiyIiIhgsshD5PEnxFfDy3VA0rhnJTIodNpUemiUW9MEn9lGsOoA0L8CQo8DrMAarVaTp48yQcffGB5Ti6X06FDBw4fPpzuMc2bN2f16tUcO3aMxo0bc+vWLTZv3sygQYMyXEej0aQqfBsTEwOYGr3rdDobnU36xCaa1nVSyjNcy0Uh4aSUo9EbeRwRR5liadt6FWSiE5LPW4kx1XUwGo3ExMQQGhoKmHqvZtUaLSMkvQ5JbyAhIQGZwbaxhppEHZJei1FnJD4+/f1JEkh6LRIQFx9v00QUowQ6remzZNAmEq+zbm6DTo+k16LRGDLcd0okSUKr1ZquoY0TaQTZQ7wX9scgGQiODSY6MQ6lVBIJLb7uCoqp3ElISADAqNcQeu86Xo8PoNRGY2j2FsbWH4JCBXb+/Sio6HQ6JMmA0Zi5QUOSDNn+Hbb3b3ZRw2ECMCwsDIPBkKYKdkBAAFeuXEn3mP79+xMWFsbzzz+PJEno9XpGjRrFhx9+mOE6c+bMYcaMGWme37dvH5cuXcrdSWTB/VgAJUa9NtMikm4KBRq9jPXbdlPew65bynMiNQBK5Ehs37qFjH7L4uLiclXvLFonQ2+E+FAJtY3t2okGiNfLiFJIRGbyLyZCK0OSQBMmkY1k3SwxSBCllSGXgf6p9dY8nRFidDKUMoh+LKyAAoEZnaQjWh+NUTIil7yR8RCVXEKvgidJYxRGDWp9DIqEp/he/5MjFSYSklgXtm536N7zOzExMdx8KMfZLfMfs8S4GLYn3sTDw/ofvbCwsNxuT5CCApUFvGfPHj799FMWLlxIkyZNuHHjBm+//TazZs1iypQp6R7zwQcfMHHiRMvjhw8fUqNGDVq1amX3QtCn70XC+WN4urnSpUvGPWGXPzhK+P0oKtduwAs1HNcWxh7cDouDUwdxdVLRtWunVK/pdDq2b99O06ZNkcvl6PX6HLtPP1hzgdMPonivUxXaVvGzxdYtrD56n9VH79GldgnGtc24uOtrP5/iUVQCX75Ui+qlbVeQ+sTdCKZvvUR5X1cWDahn9XEXHkYz/Z/zlPJyYdmQ+lmO1+v1HDp0iObNm6NUFqivhkKHeC/sg9agZf6J+aw4swIJidKqHhDbBWeVgh8G1jO15tRrkB+ah+LyOjAaUPgGsav8RFp27Sc6xVhBeHg4t/ffwtXDO9Nx8TGRdGxZIduFoAW2w2HfLMWLF0ehUPDkyZNUzz958iTDfqBTpkxh0KBBDB8+HIDatWsTFxfHyJEj+eijj9K1IDk5OVkaNgNER0cDoFKp7P6PWY/JDOSsUmS6VoCnMxBFeIKh0H3B6CTTNXBRZ3wNbPFeJKLiYYyBOKMyW3eU1hClk/MwxoBRrs50bkmh4mFMLBFahU338CgunIcxBqqVdsnWvD7e8DDGQLxBY9VxOp0OvV6Pu7t7ofscFjTEe2F7LoRcYOCagZx9chaA/tXHcOZiF+I0BqZ3r0rFUsUh7Dr8NRSeXABk0PIddM9PInHLtjz5zSgMqFQqZDIFcnnmoTgymSLb11Rcf9visCQQtVpNgwYN2Llzp+U5o9HIzp07adasWbrHxMfHpxF5CoXpQ5YfO9pZuoBkUAPQjCUTODp/lrPJDYl2LgJtxpxgYu46YksSM8hifhbz+xhm46LeDyNM8Uilsxkf6uVi+rKMTsy5ZVUgKOgYJSPfHP6Ghj805OyTsxR3Lc4/fdfgHD+EOI2B+mW9GdQsCM7+AUtam8Sfmx8MWgPtp4BcWGAFhROHfrInTpzIkCFDaNiwIY0bN2bevHnExcUxbNgwAAYPHkzp0qWZM2cOAN27d2fu3LnUq1fP4gKeMmUK3bt3twjB/ERyFnAWwsHdVAuwMHYDMReBzko85RbXpPkT7JBJbRaAzlkIeXMPYFsLwEeRSQLQO2cC0GCUiNXo8bCiH7BAUJi4H3WfoeuHsuv2LgC6VO7CTz1+4thNIzuvnEatkPNFj8ooNoyFM6tNBwW1hJeWgkf6niiBoLDgUAHYr18/QkNDmTp1KsHBwTz33HNs2bLFkhhy7969VBa/jz/+GJlMxscff8zDhw/x8/Oje/fufPLJJ446hUzR6E3CIbMyMAD+noW3FqC9+wCbMc8fr9XbfO4EnXWdTOwlAB8mCcBS2RSAzio5aoUcrcFIVIJOCEBBkeK3878xevNoIhMjcVW58vULX/NGgzeIiNcxfcNeAKY2kVFpfXcIvQLIoM370OpdyMJ9KRAUBhxu2x47dixjx45N97U9e/akeqxUKpk2bRrTpk3Lg53lHnP3BnUWAtDcD7gwdgNJsNJ9mluSXcC278SRaK0A9FADEBqjten6OXUBy2QyPF1UhMVqiErQUaaYTbclEORLIhIiGL15NL9f+B2AxqUbs6r3Kqr4VgFg1sZLhMdpeKvYMQac+wH0CeAeYLL6lW/lyK0LBHmKwwVgYcYSA5iVACzE3UAStHkTA5jsAraHBdD0PmYlYu1hAdQbjAQnxYaWyaYFEMDLRWkRgAJBYWfnrZ0MWTeEhzEPUcgUTGk1hQ9bfohKYbJ+774awtbTN5mrWkafhAOmgyq0hT4/gHva7lMCQWFGCEA7kiwArU8eMBolSwuvwoA5BjAr61lusWsSiJVubHsIwODoRIySqQWcef7sYEkEEQJQUIhJ0CXw4c4PmXd0HgCVfSqzqvcqmpRpYhkTq9Gz7O9/+Vf9JRXlj0Emh7YfwfMTIRc1SAWCgooQgHbE2hhA8w+7ziARmaDDx01t973lFQl5nAUcbw8BqLcuCcQvhQCUJMkmHRzM7t+S3s45ujEwC0BhARQUVs4En2HAmgFcCjUV9n+z4Zt82fFL3NRuyYMkiZ2rP+dH7Tc4y3UYPUoif3kZlGvuoF0LBI5HCEA7Ym0MoFopp5irioh4HaExmsIlAPMoC9jFjlnACVZaMc0xgIk6o82ybh9F5SwD2IwQgILCisFo4KtDXzFl9xR0Rh0BbgEs67mMLpWf6dGbGM3TP0bT8/6/IIPwUq3xGbAc3Hwds3GBIJ8g7N52xFoXMBTeOEBLEojavvcarnZ0AVubyOKqVuKWtI+wWNskgpgtgNnNADYjBKCgMHIn8g5tf27L+zvfR2fU0ataL86/eT6t+Ht8FuOS1vje/he9JGdTwJv4DF8nxJ9AgBCAdsVSBzAL1yGAv4epFmBITOEqBp1XdQDNAtMuLuCk99GaOMbiNi4G/TCHNQDNCAEoKExIksSKMyuos6gO++/tx13tzrIey1jzyhr83PxSDoRjP8LSDsgjbvFAKs5I5WyeHzJLxPsJBEkIF7AdsTYGEAqvBdDSRUNt3y9ds8BMtGMhaGtEbHF3J+4+jSfMRu/jw0jTDUF2S8CY8bQIQNtnRwsEeUlYfBhvbHyDNZfXANAisAWreq+ifLHyqQcmRMK/4+DSegB2GBvwjvYNPu/bCi9XUQtTIDAjBKAdES7glIWg88YFbA8LYLIb2xoBaIoDtJkFMCIeEBZAQdHmv+v/8dqG1wiODUYlVzGz7Uzebf4uimcLNj88CX8Ng8i7SHIVS52H8kl4G16sVZLOtUo6ZvMCQT5FCEA7Ym0SCIC/R+EsBh2fR4Wgze5ZW3cC0RmMGIymPrrOVgh5c0Z3qA1iACVJEi5gQZEmXhfPu9veZeGJhQBUL16dX/r8Qr2S9VIPlCQ4sgi2TwWjDoNnIH8EzeKTY2o8nZXM6FnTAbsXCPI3QgDaEeECTq6hZ/dC0GqzC9i2nUBSZhU7W+HGtmUtwIh4neV8Sno752gOUQdQUFA59vAYg9YO4trTawC83eRtPmn7KfFaBecfRPEoKoHgqETCnwbT4epMasceBGCrsTHvhowgOsRkjf+4aw1LjLVAIEhGCEA7Ym0nEEiuIRdq4z6yjiY+qTNHXpWB0RqM6A1GlArbxBya4/9kMlMx5qywJIHYQMibM4D9PJysCiNID3PMU24tgOcfRHEvPJ6udYQbTWBf9EY9n+7/lJl7Z2KQDJT2KM3XHZbz825n6u3fi9aQfJNXX3aN79TzKSMLQyMpma0fyCpDR2QyGQEeTnSvU4q+Dcs48GwEgvyLEIB2JDkLOOsfb3/PJBdwdOHKAk6wsotGbkk5f4LOgIetBKA2uQ2cNYWd/ZJiAG0h5B9G5i7+D1K7gHNanDoqXkf/H48Qo9FTrWRrKvq553g/AkFmXH96nUFrB3H04VEA+tXsx8KuC/l0430uPX4AmG7G/NxUjFb/x6D4n1FgIMqlDGebzqNnuQaM8nbB38MJlY2+AwSCwooQgHbEfKdqjeXIz93koohO1JOoM9i9dVpekVeFoJ2UcuQyMEqmNW1RhBmsrwFoxs+GZWBymwEMyQLQYJSI0xpwd8r+P/nlh24TozFZch9EJAgBKLA5kiTxw8kfmLhtIvG6eLycvFjYdSH9a/fndlgca06ZxN/K1xrTtASo/x0N17eZDq7ZB6/u39LK2dOBZyAQFDyEALQjlhhAK+oAerooUSvlaPVGwmI1lCnmau/t5Ql51QpOJpPholIQpzXYtBuI2QVsrSC3xADG5D4JxOwCzo0F0EWlQKWQoTNIRCXosi0AoxN1LDtw2/I4PK5whSgIHE9wbDDDNwxn0/VNALQNasvPvX4m0CsQgO92XscoQbtq/rRyug4/vg4xj0DhBC9+Dg2GmsyCAoEgWwgbuR2xuICtiAGUyWTJcYCFKBEk3so2arbAHsWgE3TW9QE2YxaACToDcZrcZSTbwgUsk8nwcjG5paPisx8HuPLQHaITk8/jqY06nAgEAOuurKP2otpsur4JJ4UTc1+Yy47BOyzi70ZIDOvPPESGkU+Lb4MV3Uziz7cyjNgFDYcJ8ScQ5BBhAbQj2akDCCb34cPIhEJTCsZolCzXwN4WQEguNm0PAWhtDKObkxIXlYIEnYGwWA1uOXC5mnlkdgHnQgACeLkoCYvVZDsRJFajZ2mS9a+0twsPIxNs1uJOULSJ0cQwfst4lp1ZBkDdgLqs7rOaWv61Uo37ducNiklRrCz2EyVOnDA9WacfdJ0LTiIUQSDIDcICaEeyUwYGCl8pmJSuWHsngQC4qkxiy5bdQDRmC2A2snCLe9imGLS5BmBO+wCbyWktwFWH7xIZr6NCcTdeaWiyyAgXsCC3HLx3kLqL67LszDJkyJjcfDJHhx9NI/6uBscQdn47/zl9QM2EE6B0gZ4LoPcSIf4EAhsgLIB2RJuNMjCQXAy6MArA7AionOJih24g2bUAgskNfD88gdBcxAHGa/WEx5mOz00SCOSsFmC8Vs+P+28BMLZdJYslV7iABTlFa9AyY88MPjv4GUbJSDmvcqzsvZJW5VqlHWw0cP3Pj1itWolCJoFfNei7Avyr5/m+BYLCihCAdiQnLmAoPN1AErTJ8XNyuf3jdMyZurZMAklIKgOTnRhGWxSDNrt/PZyUFgGXU3JiAfzlyD3C47SU83WlR91S7LoSAkBYnBCAguxzOfQyA9cO5NTjUwAMrjuY7zp/h5ezV9rBMcHE/fYa3cIPggyiqvbD66VvQO2Wx7sWCAo3QgDaEYsAtDKBoLC6gO1dAsaMOc4wwYbt4LKbBQy2EYC2cv9C9gVggtbAkn0m69+YtpVQKuT4JtU3fFrICpUL7ItRMjL/2Hze2/EeifpEfFx8+KHbD7xU46X0D7i5C9aMxC0ulDjJiX9KvsPg/72Xt5sWCIoIQgDaCX2KHrJWxwAWsm4gCZY2cHnzMXO2pwvYShEPKYpB50LIW0rA5NL9C9kXgH+cfJBUisiF3vVKA+DrZvpshgsLoMBKHkY/ZNj6YWy/tR2AzpU6s6zHMkp6pNNNxqCHPXNg/9eAxGVjIG/px7G4T/+83bRAUIQQAtBOpGxXZK0L2N/TVAw6tJB0A4nXZq+ESm5xtYMLWJMDC6AtikE/isx9DUAzntkQgDoj/Lj/DmCy/pm7KZgtgPFaA/FafZ6JekHB5M+LfzJq4ygiEiNwUbrwZccvGd1odPqdaKIfwd+vw71DAOx278aosJfpUq88lfxFsodAYC/Et7idMNcABFBnNws4VpPjtl35iURd3loAk13A9rAA5sQFnHNrmaNcwEdCZITEaCjl5cxL9ZN7qLo7KVEr5GgNRp7GanH1EV8dgrREJkYydvNYfjn/CwANSzVkVe9VVCteLf0Drm+HtW9A/FNQe3C7+ScM21IchVzGuPaV83DnAkHRQ5SBsRPm+D+lXIbCygSI4klWFnPXhoJOfB61gTPjbEcBmK0YQBtYAB3hAtbojex4aPpKeLNtpVQ3LjKZzGIFFG5gQXrsvr2bOovq8Mv5X5DL5ExpNYVDrx1KX/wZdLB9Kvzyskn8lagDb+xl2i1Tlm/veqUpX1wkfQgE9kTcxtuJ7NYANI1V4O2qIjJeR0iMBm9Xtb22lyfkpIRKbjDXAYy3aSu4XGQB5yYG0IYuYGvLwKw5/ZBIrYwATydeaVgmzeu+7moeRyXyVNQCFKQgUZ/Ix7s+Zu7huUhIVCxWkVW9V9EssFn6B0Teh39eh/tHTY8bjYAXZnPiYTz7rl1BKZcxrp2w/gkE9kYIQDuRnAGcPfHj5+5EZLyO0BgNVQI87LG1PMOcjZtXFkBzJ5BEByeBmC25cVoDCVpDtgWw3mAkONo2XUAAvFyztgBq9UYW7zV1/RjZsny6cas+brl3bQsKF+eenGPgmoGcDzkPwIj6I5jbaS7u6gxi965shnVvQmIkOHlBz++hRk8AvtlxBoC+DctQ1rdw9EIXCPIzQgDaCXMRaLUie152Pw8nrofEFopSMAmWGMC8EoC27wVsFpPZEXHuTkqclHI0eiNhsRoCfbL3Y/YkRoPBKKFSyCzFwXNDShdwRrGla08/4FFUIp4qiVcalE53nuJuwgUsMGEwGph7eC4f7/4YrUGLn6sfS3sspUfVHukfoNfCjulwZIHpcan68PIy8CkPwJFbTzl44ykqhYwxbSvlzUkIBEUcIQDthMUFnM0MWH9LMeiCnwlsyQLOKwGYZGm0qQtYn/0YQJlMRnF3U1/n0BwIQHMGcEkvF5sU0DYLQL1RIl5rSNOfWGcwMn/3DQDalTJmeK6iFqAA4G7kXYasG8Leu3sB6F6lO0t7LMXfzT/9AyLuwN+vwcOTpsdNR0OHGaA0fZ4kSWLu9msA9GsUSJliwvonEOQFQgDaCXMWcHZiAKFwFYO2WADzuBC0TV3A2uwLQDAlgjyMTMhRHKA5AaSUt3O2j00PF5UClUJmSS56VgCuP/OI++EJ+LqpaREQn+E8ZhewaAdXNJEkidXnVjP2v7FEa6JxU7kxr/M8Xq/3esYVCy5tgPVjQRMFzl7QaxFU65pqyOGbTzl2Oxy1Qi6sfwJBHiIEoJ3Ibhs4M4VKAObAfZobLL2AdbbrBJKQJOSzG8eYm6LeyQkgtrGEyGQyvFxUhMVqiUrQpSotozcYWZBk/Xv9+XKooy9nOI/FAihcwEWOp/FPeXPTm/x16S8AmpVpxqreq6joUzH9A/Qa2PYxHPvB9LhMI5PL17tsqmEprX/9m5SlpFfuY14FAoF1iDIwdiJZAGbXBWyy+hSGfsB5LgBVti8Dk5NC0AB+HiaxFBaTfbFkEYA2KAFjJqNi0BvPPeZ2WBzFXFX0bxSY6RzFLQKw4H827UVhuHF7lq03tlJ7UW3+uvQXSrmS2W1ns2/YvozF39Ob8FPHZPHX4m0Y9l8a8Qew/3oYJ+5G4KSUM7pNBvMJBAK7ICyAdsIcA2htEWgzhcoC6LBewI4tBA256wdsqQFoIxcwpF8L0GCU+H7XdQCGt6yQxjX8LL7CBZwhkiQxbcNFVh6+y9i2lZjUqaqjt5Rr4nXxvLf9PeYfnw9AteLVWN17NQ1KNUgzVqs3olLIkF1cCxvGgTYGXHyg9xKo8kK680uSxNdJ1r9BTctZOiEJBIK8QQhAO5FTC2DKbiAFnYQ8LgRtlyQQiwUwe+9jrgSgjV3AkL4A3Hz+MTdD4/ByUTG4Wbks5/BxS3YBF4ZONbZCkiRmbrzEysN3AVi09yZdapekRilPB+8s55x8dJKBawdyJewKAG81fovPOnyGqyrtZ3LXlSe8veoIHytX0k+2A4BHns9x9fl5BHpXoqzemO6N8O6rIZy9H4mLSsEbrYX1TyDIa4QAtBM5jQE0ZwFHxuvQ6A3ZPj4/kdeFoF3ySScQyLkAlCQpuQ+wDV3AzxaDNqaw/r3+fHk8nFXodJkXijbHAGr1RmI1ejycVTbbX0FFkiQ+23KF5QfvAFDZ353rIbFMWX+Bv95oZpMs7rxEb9Tz+YHPmb53OnqjnpLuJVneczmdKnVKd/ztsDi++W0zfyrmUl12D6MkY6GhB9+EvIxhzSPgEQq5jHI+rlT0d6einzsV/dyo5O/ON9tNn7/BzctZbnwFAkHeIQSgnTDHjmW3DIyXi8qSsRkWq7VJIWBHkdet4Mw9hzV6IwajZHULvoyQJMnSCSS7ItYcL5fdosmR8TrLdSvpZT8X8NaLwVx7EouHs5IhzYOsmsNVrcRVrSBea+BprFYIQOCb7ddYsvcWALN71aJ9dX/af72Xk3cj+PvUA15pmHlcZX7iZvhNBq0dxOEHhwHoW6Mvi7ouwtfVN93x8Vo9f/z0Nb8zHze5Br2zL6cbfoFM8Rw9Q2K5GRrLjZBY4rQGboXFcSssju08STWHm1rBG62E9U8gcARCANoJrSFnhaBlMhl+7k48ikokNEZToAVgoqUQdN58zFIKzURd2np32cVsxYWclYGB7LeDM7t/i7s7ZXvNzEgpAI1GiW93mqwvw1qUt7xmDT5uauK1CTyN0xJUxHu1fr/zOt/tMmVQT+teg4FNTW708R0q8+nmK3z23xVeqBGQ71s6SpLET6d/YvyW8cTp4vB08mT+i/MZWGdghm5+SRvHmQXDeT9hM8hAW6Y56n7LaeRRgkbPzP0kWmMRgyn/GxKj4e0OlS2hBQKBIG8RWcB2wlIHMJsWQAC/pGDogp4IYrEAqvPmY5YyTs8W3UBSupKdsxnLaXYBx2j0FiFsDfbIAIbUAnDH5SdcCY7B3UnJay2CsjWPr7s5EaRgfzZzy5K9Ny0JDB+8WI1hLcpbXhvWojxVAtwJj9PyxdarjtqiVYTEhdDrj16M+HcEcbo4WpdrzblR5xhUd1DGMZ4hV4j4tiXNozZjlGQ8qvs26tc2gkeJNENlMhklvJxpUak4Q5oHMbNnLX4d0ZSjH3bgxiddGCmsfwI7MmfOHBo1aoSHhwf+/v706tWLq1dT/5tMTExkzJgx+Pr64u7uzksvvcSTJ6kt1ffu3aNr1664urri7+/Pu+++i16futzYnj17qF+/Pk5OTlSqVIkVK1ak2c+CBQsICgrC2dmZJk2acOzYMZufc3YQAtBO5DQGEJJryBX0biDJGbR5YwGUyWQ2LQVj3r9aIUeZTUuup7PSYv3NjpC3RwYwJJeBiYzX8V1S7N+Q5uWybZ0q7iZqAS47cJs5/5mSIya9UCVNAoNKIWdWz1oA/HbsHqfvReT5Hq3h36v/UntRbTZc3YBaoebLjl+ya8guynlnkhB0+hcMS1rjE3eTEMmbbQ2XUKr3TJBn/3sutyEaAkFW7N27lzFjxnDkyBG2b9+OTqfjhRdeIC4uzjJmwoQJ/Pvvv/z111/s3buXR48e0adPH8vrBoOBrl27otVqOXToED///DMrVqxg6tSpljG3b9+ma9eutG3bljNnzjB+/HiGDx/O1q1bLWP++OMPJk6cyLRp0zh16hR169alU6dOhISE5M3FSAchAO2EpRVcNi1HUHhKweR1HUBIUQrGBpnAiTmM44QkV75H9hNBkjOA7WMBPHr7KRceRuOqVvD68xWyPY9PAe8HrNEbWLjnBtsvPcFglLJ9/Kojd5m58RIA49pVYmy7yumOa1LBlz71SyNJMGX9hRytZS9itbGM/HckPX7vQUhcCLX9a3N8xHEmNZ+EXJbBZ10TC2tHwfrRKAyJ7DfU4rvKy+jU7ZW83bxAkA22bNnC0KFDqVmzJnXr1mXFihXcu3ePkydNbQmjoqL46aefmDt3Lu3ataNBgwYsX76cQ4cOceTIEQC2bdvGpUuXWL16Nc899xwvvvgis2bNYsGCBWi1pu/BxYsXU758eb7++muqV6/O2LFjefnll/nmm28se5k7dy4jRoxg2LBh1KhRg8WLF+Pq6sqyZcvy/sIkIQSgnciVBbAQCEBJkpJbweWhADTHzcVrc98NJLd1DHOSCPLIzgLQnNQyqFm5HMVe+eaivE1+YNXhu3yx5SojVp6g3dd7WH7wNrEa6z4rfxy/x5R1FwB4o3UFJnSskun4D16sjqezkgsPo/nl6N1c790WHL5/mOcWP8ePp35EhoxJzSZxbMQx6gTUyfigJxfhx7Zw9jcMyPlS9wpzfD/lo1faiFJAAocQExNDdHS05U+jse77KCoqCgAfHx8ATp48iU6no0OHDpYx1apVo2zZshw+bEqGOnz4MLVr1yYgIMAyplOnTkRHR3Px4kXLmJRzmMeY59BqtZw8eTLVGLlcTocOHSxjHIEQgHZCmyQAs1sIGpJLwRTkbiBag9Fi9bBlMkNW2NYCaHoPc7r/nJSCMVsAS9lJAIIpVnJEy+xb/yBFN5ACWgx61xWTu0Uug7tP45nx7yWafbqT2RsvcT884z7Ia0494P015wF4rUV53u9cLUvx4+fhxLtJBaG/3HrVoSEdOoOOKbum8Pzy57kZcZNAz0B2DdnFly98ibMyg3ADSYKTK+DHdhB2jWhVcf6n+YhVqpdZNKhhnlr2BYKU1KhRAy8vL8vfnDlzsjzGaDQyfvx4WrRoQa1aphCN4OBg1Go13t7eqcYGBAQQHBxsGZNS/JlfN7+W2Zjo6GgSEhIICwvDYDCkO8Y8R2YkJCQQH5/8/XT37l3mzZvHtm3bsjw2M0QWsJ3IaSFoKBwWwERtcgZtXloAbdkNJDHXFkDT+/jjvlv8dyHrf+QAV4NjAPslgQAMbFLOsrfsUpBdwDGJOo7fCQdg07iWnLgbwfIDt7kVFsfSA7dZdvA2nWqW4LXny9OwXDGLwPv37CMm/XUWSTJ1rJjSrbrVlq/+Tcrx54kHnH8YxZzNV/im33P2Or0MuRp2lYFrB3Li0QkABtYZyPcvfo+3s3fGByVGw8bxcOEfAB77t6TrvQFEyDxZ9mo9yvkW7QxwgWO5dOkSpUuXtjx2csr6+2zMmDFcuHCBAwcO2HNrdqFnz5706dOHUaNGERkZSZMmTVCpVISFhTF37lzefPPNHM0rBKCdyGkdQCgcAjBeZ3KrKeUyVNlMoMgNZmudLSyAZhHpnEMBW8nfHcBSA81aXFQKm//A+rip8XJRoTcYGdkqZ9Y/KNgu4IM3nqIzSFQo7kb1kp5UL+nJgMZl2XstlGUHb7P/ehj/XQjmvwvB1CnjxWstyqOQyxj/xxmMErzaKJAZPWpmy+2pkMuY3asWvRYeZO3ph/RrFEjTCunX1bM1kiSx8PhC3t3+Lgn6BIo5F2Nxt8W8UjOLuL3HZ+GvoRB+C2QKghtNpu2h2iQC49tXpm01/7zYviAfYDQaiYyMtGqst7c3cnnefNd7eHjg6Wl9p52xY8eyceNG9u3bR5kyZSzPlyhRAq1WS2RkZCor4JMnTyhRooRlzLPZuuYs4ZRjns0cfvLkCZ6enri4uKBQKFAoFOmOMc+RGadOnbLEE/79998EBARw+vRp/vnnH6ZOnSoEYH4jNzGA/ikEYEFtueWIBBBItgDaogxMYlIiT3ZLwJgZ3LwcZX1dibMyxsxM9ZKeuOeyhuGzOKsUrBndHLlMlqueq74FOAt4z1WT+7dN1WQBI5fLaFvNn7bV/LkaHMPyg7dZc/oh5x5EMf6PM5ZxfeqX5tPetXPU2aNuoDf9G5fll6OmGMJN41rmKDQkOzyKecRr619j601TFmLHCh1Z3nM5pT1LZ3yQJMHxpbD1QzBowbMM0d2W8PJaLYn6BNpV82dcBkkvgsJJZGQkczeextnNI9NxiXExTOxWzxJbl1+QJIm33nqLtWvXsmfPHsqXL5/q9QYNGqBSqdi5cycvvfQSAFevXuXevXs0a9YMgGbNmvHJJ58QEhKCv7/pu2P79u14enpSo0YNy5jNmzenmnv79u2WOdRqNQ0aNGDnzp306tULMInrnTt3Mnbs2CzPIz4+Hg8P03uwbds2+vTpg1wup2nTpty9m/P4YiEA7YQ2Fy5gs3tOazASnaDHy7XgdVzIbQJFTrFlO7jcilgnpYJONbO+u8srKvq553oOczu4iDgtRqNUYFqdSZLE7iQB2LaaX7pjqpbw4LOX6vBup6r8evQeK4/cJTRGQ/e6pfjy5bq5OtfJnaqx5UIw10NiWXbwNqPs2Pv2n0v/MHLjSMITwnFWOvNFhy8Y03hMxhm+AIlRsOEtuLTe9LjKixh6LmTsHzd4EBFFWR9XvnnluQLzfgtsh7ObB26e3o7eRo4YM2YMv/76K+vXr8fDw8MSb+fl5YWLiwteXl68/vrrTJw4ER8fHzw9PXnrrbdo1qwZTZs2BeCFF16gRo0aDBo0iC+++ILg4GA+/vhjxowZY3E9jxo1ivnz5zN58mRee+01du3axZ9//smmTZsse5k4cSJDhgyhYcOGNG7cmHnz5hEXF8ewYcOyPI9KlSqxbt06evfuzdatW5kwYQIAISEh2bKEPosQgHbCXAYmJ3f6zioFns5KohP1hMYmFkwB6CALoLnmoC3LwOS1iM3PmGMA9UaJ6ERdvu9yYebS42ieRGtwUSloXD5zK4WvuxNvta/MG60rcj0khuolPHMtfLxcVbz/YjXe/fsc3+64To+6pWye6BOVGMW4LeNYeXYlAPVL1md179VU96ue+YEPT8JfwyDyLshV0HEGNB3NN9uuse9aKM4qOUsGNSiQ30OCos2iRYsAaNOmTarnly9fztChQwH45ptvkMvlvPTSS2g0Gjp16sTChQstYxUKBRs3buTNN9+kWbNmuLm5MWTIEGbOnGkZU758eTZt2sSECRP49ttvKVOmDEuXLqVTp+Qe2v369SM0NJSpU6cSHBzMc889x5YtW9IkhqTH1KlT6d+/PxMmTKB9+/YWy+K2bduoV69eTi+PEID2IjdJIAD+ns5EJ8YSEq2hkn/m5vf8iKMsgDZ1AecyC7gw4qRU4OGsJCZRT1istsAIwD1XQwFoUam41WEZaqWcmqW8bLaHl+qX4c8T9zl+J4KZ/15i8aAGNpt73919DF47mLtRd5HL5Hzw/AdMbT0VtSKT90eS4Ohi2DYFjDrwLgsvr4AyDdh2MZj5u01t7j7rU4fqJXNuZRAIHIUkZV1/09nZmQULFrBgwYIMx5QrVy6Ni/dZ2rRpw+nTpzMdM3bsWKtcvs/y8ssv8/zzz/P48WPq1q1reb59+/b07t072/OZEWVg7ERuYgAhuRtIaAEMtoeUbeAc4wLOTvu1jDCLWCEAU+NbADOBd1/J3P2bF8jlMmb1qoVCLmPLxWCLSzo3aPQaJm+fTJsVbbgbdZcKxSqwb+g+Zrebnbn4iw+H3wfAlvdN4q96d3hjP5RpwK3QWN758ywAQ5sH0ateJnGDAoHA7uzatQtvb2/q1auXKtGmcePGVKtWLcfzCgFoJyydQHKQBQwFPxM40QFFoCHZ4pgfCkEXVgpaP+DIeC2nktqxpUwAcQTVSngyrHkQANPWX8zVjcqFkAs0WdqELw99iYTE6/Ve58wbZ2hRtkXmB94/DktawdVNoFDDi1/CK6vAxZu7T+MYvOwYMRo9jYKK8VHXLNzHAoHA7vTo0QNvb29atmzJlClT2LFjBwkJCbmeVwhAO2EpBJ3DEigFvRi0xQLooCQQ27iAzRZA8c8kJWYLYFgBsQDuux6GUYKqAR4277CSE8Z3rEKApxP3wuNZtOdmto83SkbmHp5Lgx8acPbJWYq7Fmdtv7Us7bEUD6dMwkWMRjj4HSzvDFH3oVh5eH0bNBkJMhlXg2Pou/gwDyISKOfryoL+9fO0hJNAIEifiIgIdu7cyYsvvsixY8fo3bs33t7etGjRgo8//jjH84p/3XbC7ALOqXgo6BbA5CSQvA0zdbWhC1gkgaSPORM4vIB0A9mT5P5t40D3b0rcnZRM6WYqH7Fo703uZKNG5L2oe3RY2YF3tr2D1qCla+WuXHjzAr2q9cr8wLin8NursH0KGPVQsw+8sQ9KmQLIz9yPpN8PhwmJ0VA1wIO/3miWq3JBAoHAdqhUKlq0aMGHH37I1q1bOXLkCP/73/84duyYVV1QMkIIQDuh0eUyBrCgC0CLeMrbj1iyC1gkgdgLX7ckF3Bc/v9sGo0Se66ZEkDaOtj9m5KutUvSsnJxtHojH6+7YPEYZIQkSfx6/lfqLKrD7ju7cVW5sqTbEv79378EuGeRRXj3MCxpCde3gsIJun0DLy8DZ1Nix6GbYQz48QiR8TrqBnrzxxtNhfgTCPIR165d44cffqB///6ULl2a1q1bExUVxVdffcWpU6dyPK/IArYTlhjAnGYBe5i+gB3ZPzQ3mC2ArnlsAbRHHcCcdgIprPgWoH7A5x5GER6nxcNZSYNyxRy9HQsymYwZPWrSed5+DtwIY8DSIywc0MBy45eS8IRwRm8azR8X/wCgSekmrOq9isq+WRRlNhrh4Dew6xOQDOBbCfqugBK1LUN2XHrC6F9PodUbaV7Rlx8GN7R5EXKBQJA7qlWrhp+fH2+//Tbvv/8+tWvXtkmDCGEBtAN6gxFjUvZ5UbUAmi1weW09s/QCtmEWsHABp8aSBFIALIDm7N9Wlf3yXTxbBT93lgxqgIeTkuN3Iuj+/QHO3o9MNWbn7Z3UWVSHPy7+gUKmYEabGRx47UDW4i82FH55CXbONIm/Ov1g5N5U4m/9mYe8sfokWr2RDtUDWDa0kRB/AkE+ZNy4cZQuXZqZM2cyatQoPvroI7Zt20Z8fHyu5s1f34iFBE0Kd05OWz6ZBWBEvC5L91B+JMFBWcDONnUBiySQ9LC0gysAFsDk9m/5I/7vWdpW82fd2BZU9HMjODqRvksO8/fJByToElj6YCkv/vYiD2MeUtmnModeP8TU1lNRyrMQabf3w+Ln4eYuULpAj/nQewk4JXeCWX3kLuP/OIPBKNHruVIsGlhfhDoIBPmUefPmcerUKYKDg/nggw/QarV89NFHFC9enBYtssj6zwRxu2cHbCEAvV1UqBQydAaJp3EaSno5PnsxOzgqgcLscraFC1gkgaSPxQWcz7OAQ2M0nH0QBUDrfCoAwdSib92YFkz44yw7Lj9h0l9nUbrv56b+P5DB6Iaj+aLjF7ip3TKfyGiAfV/B3s9AMkLxqvDKz+CfupTLoj03+XzLFQAGNS3HjB41RYs3gaAAYDAY0Ol0aDQaEhMT0Wg0XL16NcfzCdOGHTDH/6kUMhQ5/GKVy2WWnsAh0fnf1fYs5jp8ed8KTriA7Y05CSQiXovBmHWlfUexNyn5o3ZpL0tMbX7Fw1nFogHP0aDyEwD0sS0prZvDLz03sKDrgqzFX8wTWNUL9nxqEn/PDYSRu1OJP0mS+HzLFYv4G92mIjN7CvEnEOR3xo0bR506dQgICOCNN97g0aNHjBgxgtOnTxMaGprjeYUF0A7kNgPYjJ+HE4+jEgtkHGBC0jVwVCs421gAk95HIQBTUSypJ6wkmUSg+UYlv2HutNE2H1v/zNyOuM2gtYM4+OAgLuqmlNBPRmmowfz/nKnjF0Wt0pm0pLu5G9aMgLhQULmasnzrvppqiNEoMXXDBVYfuQfAe52r8WabivY8JYFAYCMeP37MyJEjadOmDbVq1bLZvEIA2gGtIakIdA7dv2YKcju4BEdZAFMkgRiNUq6sG8ICmD5KhZxirioi4nU8jc2fAlBvMLIvyQLYplr+Kf/yLJIkseLMCsZtGUesNhYPtQffvziKRsVbM3jpIR5FJfLy4kN8/lIdej73TEs2g97k7t33FSCBf01Tlq9flVTDdAYj7/51lnVnHiGTwexetRjQpFyenaNAIMgdf/31l13mFQLQDiRbAHMnAP09C64L2CKeHOQCBlMsZm7WT3RQP+OCgK+7U5IA1ACZdJ9wEKfuRRKTqKeYq4q6ZbwdvZ10CY0L5Y2Nb7D2yloAWpZtyc+9fqZ8sfLodDreqW1gS2QJ9l4P4+3fz3DhYRTvda6GUiGH6Efwz3C4e9A0Wf0hGDp9xqM4uHs9jNtP47gTFsfdp3FcfRLD/fAElHIZX79SN62QFAgE+Z5Vq1axePFibt++zeHDhylXrhzz5s2jfPny9OzZM0dzCgFoB3JbA9BMsgWw4NUCdFgruBTrxWv1uROAepEFnBE+bvk7EcTs/m1dxS/Hcbj2ZNO1Tby+4XWexD1BJVcxu91s3mn2Dgp58ufVVQlLBtbju923WLjnJj/uv83lxzF8VvcJATveRqUJRyN35Wff8fxxvQn3j+61eB+exVklZ0H/+rSvnkXRaIFAkO9YtGgRU6dOZfz48XzyyScYDKbfJm9vb+bNmycEYH7CnAVsixhAKJi1ABO1jikDI5fLcFLK0eiNxGsN+OZwHr3BiM5gSnAQLuC0FLcUg86fn01z/b+2+cz9G6eNY9K2SSw+uRiAmn41Wd1nNc+VeC7d8Qq5jMmdq1GzlBfv/3WSFne+p8yDfwG4aCzHGM047twvCZjayakVcgJ9XChf3I1yvm4EFXcjyNeVmqW8LKJdIBAULL7//nt+/PFHevXqxWeffWZ5vmHDhkyaNCnH8woBaAfMdfuccmk58rN0A8mfP7KZEe/A+DlXtQKN3pirfsCJKUr5iPpoaUluB5f/LICPoxK4EhyDTGYqAJ1fOPrgKIPWDuJ6+HUAJjSdwKftP8VZmXWGctdyBtqW+gbXJycAWKfqwn8lx9Lev5hF5AX5ulHK2yVfWjwFAkHOuX37NvXq1UvzvJOTE3Fx1vcSfxaH+7YWLFhAUFAQzs7ONGnShGPHjmU6PjIykjFjxlCyZEmcnJyoUqUKmzdvzqPdWofZBazOZeeBgmwBTHBg/Jy5FmBuikGnzCLOrSu/MJKfXcB7rpqSP+oFelMsH1i9dAYd0/dMp8WyFlwPv04ZzzLsGLSDuZ3mWiX+uPofLH7eJP6cPDG+/DO9PvqNJa+1YEq3GgxqWo6Wlf0I9HEV4k8gKISUL1+eM2fOpHl+y5YtVK9ePe0BVuJQC+Aff/zBxIkTWbx4MU2aNGHevHl06tSJq1ev4u+f1nWj1Wrp2LEj/v7+/P3335QuXZq7d+/i7e2d95vPBI2NLID+KQSgJEk26f2XFxiMkuUaOMICaI7Zy00twJRFoAvKdc9L8rML2Oz+bZcP3L/Xnl5j4JqBHH90HID/1fofC7osoJhL1n2JZUY98h1T4Ogi0xOl6sHLy5H7lLfnlgUCQT5j4sSJjBkzhsTERCRJ4tixY/z222/MmTOHpUuX5nhehwrAuXPnMmLECIYNGwbA4sWL2bRpE8uWLeP9999PM37ZsmWEh4dz6NAhVCpTLbKgoKC83LJV2LIOIJgEZXSiHi8XVa73lhekdL2arXF5iS26gYg2cJlj6Qecz9rBafQGDt4IA6BNVccJQEmSWHxiMe9se4cEfQLezt4s7LKQ/9X+n3UTRN6l5fXZKOJvmR43HQ0dpoMy/5XcEQgE9mX48OG4uLjw8ccfEx8fT//+/SlVqhTffvstr776atYTZIDDBKBWq+XkyZN88MEHlufkcjkdOnTg8OHD6R6zYcMGmjVrxpgxY1i/fj1+fn7079+f9957D4UifbGl0WjQaJKtFDExMQDodDp0Op0NzyiZeI3pR1ElJ1drKAAPZyUxiXoeR8ThqsyiG0A+ITo++XrLJQM6XfqZieZrY+v3wUlpstjFJGhyPHdMgukcnFUKu31O8hvZeT88nUzC+Glszq+xPThy8ylxWgP+Hk5U8XNxyN6CY4MZuWkkW25uAaBtubYs7b6UQM9Aq/Yju7IJ5ca3KKaJRnLywtD9e6SqXUAC8tG1LirY63uqIKDT6ZAkA0Zj5jfTkmSw/Kbm5Jjs7KeoMmDAAAYMGEB8fDyxsbHpekmzi8MEYFhYGAaDgYCA1GUJAgICuHLlSrrH3Lp1i127djFgwAA2b97MjRs3GD16NDqdjmnTpqV7zJw5c5gxY0aa5/ft28elS5dyfyLpcOaRDFAQFhKc6/hEFxTEIGPjjn1U9sq/bbdS8jQRQIlaLrFly39Zjt++fbtN14+LkgNyjp44jXQvZ9fsRjSAEoMmId/FmNoba96P4HgAJcGRcfnq+qy9Y3rvyzsn8N9/WX/2bM3hyMMsvL+QGEMMKpmKQSUH0c27G+cPnOc85zM9Vm7UUfPR71QINV3/cNeKnCg/hoSbwM38c42LKrb+nioIxMTEcPOhHGe3zGt9JsbFsD3xJh4eHjk6xlrCwsKsHltYcXV1xdXV1SZzFagsYKPRiL+/Pz/88AMKhYIGDRrw8OFDvvzyywwF4AcffMDEiRMtjx8+fEiNGjVo1aqV3dzH9/begrs3KF82kC5dauZqrl+DjxNyO4IKNZ+jS52SNtqhfbn+JBZOH8LNWU2XLm0zHKfT6di+fTsdO3a0uPRtwaaoM1yODKFy9Zp0aVI2R3Psvx4GF0/h6+1Jly7NbLa3/Ex23o+IeC1zzu4hwSCjwwudc931xlZ8++0BIJ6B7evRuWbe1byL1kQzcftEVt5ZCUDdgLqs6LGCmn5W/vuPuI1izXDkoWcB0DV6kwO6RnR44UWb/tsQZB97fU8VBMLDw7m9/xauHt6ZjouPiaRjywr4+Pjk6BhruXPnjtVjCzr169dn586dFCtWjHr16mUai37q1KkcreEwAVi8eHEUCgVPnjxJ9fyTJ08oUaJEuseULFkSlUqVyt1bvXp1goOD0Wq1qNVpM/6cnJxwckqOm4mOjgZApVLZ7R+zTjK9US5qZa7X8Pd0ASIIj9cXmC8fbdL5u1p5/rZ+L9ydTHNpjeR4Xq0x6Ryccv8eFjSseT+KeyhRyGUYjBKxOomAfBCfevdpHLfC4lHKZbSuFpBn79v+u/sZvG4wdyLvIEPG5BaTmdl2JmqFlRnIF9bAhnGgjQEXH+i9GMq3Q9q82a7fU4LsURTfC5VKhUymQC7PPJ5dJlNYrk9OjsnOfooKPXv2tGiXnj172iUZ0WECUK1W06BBA3bu3EmvXr0Ak4Vv586djB07Nt1jWrRowa+//orRaEQuN1kcrl27RsmSJdMVf47CVp1AoGD2A3ZkCZiU6+amDEyi6AOcKXK5jGKuasJiNYTFagjwtKKciZ0xl39pGFQMT2f7/1BoDVqm7Z7G5wc/R0IiyDuIlb1W0rJcS+sm0CXC1g/gxDLT47LN4KWfwKu0iPUTCIo4Kb2a06dPt8saDvXbTJw4kR9//JGff/6Zy5cv8+abbxIXF2fJCh48eHCqJJE333yT8PBw3n77ba5du8amTZv49NNPGTNmjKNOIV0sWcA2yCA19wMOLUD9gBN0esBx4sm8rsgCti/JpWDyRyawuf1b2zzI/r0YcpEmS5vw2cHPkJAY+txQzo46a734C7sBSzski7/nJ8KQjSbxJxAIBCkYPnw4e/bssfm8Do0B7NevH6GhoUydOpXg4GCee+45tmzZYkkMuXfvnsXSBxAYGMjWrVuZMGECderUoXTp0rz99tu89957jjqFdDH341RnkJmcHQqmBTCpBqCDLIDm9nO5qQOYYBGAwgKYEeZi0OH5oBh0gtbA4ZtPAfu2fzNKRr47+h3v73gfjUGDr4svP3T/gT7V+1g/ybk/4d/xoIsD1+LQZwlU6mC3PQsEgoJNaGgonTt3xs/Pj1dffZWBAwdSt27dXM/r8CSQsWPHZujyTU/xNmvWjCNHjth5V7nDlhbAgtgNJF7rWAugs01cwKb3UAjAjDHXAgzLBzcnR249RaM3Utrbhcr+7nZZ40H0A4auG8rO2zsBeLHSi/zU4ydKeliZnKWNh/8mw+lVpsdBLaHPj+BZMJK7BAKBY1i/fj0RERH89ddf/Prrr8ydO5dq1aoxYMAA+vfvn+OEVuHfsgO2jAE0u4ALUj9gs/vU1VEWQJXtLIAiBjBjfPNROziz+7dNVT+7BEv/dv43ai+qzc7bO3FRurCo6yI29d9kvfgLuQI/tksSfzJo/R4MXi/En0AgsIpixYoxcuRI9uzZw927dxk6dCirVq2iUqVKOZ7T4RbAwoilFVwuO4FAsgs4PE6LzmBElcv+wnmB2fLmKPFky04gjnJjFwTMAjDcwTGAkiSxy07t3yISIhizeQy/XfgNgEalGrG6z2qq+FaxfpLTv8DmSaCLBzd/eGkpVGht030KBIKigU6n48SJExw9epQ7d+6kqaWcHWyqJhISEmw5XYFFaxGAub+8xVzVlgbv+SXYPisSHCyezC5gmySB5JP6dvkRSzu4OMdap2+GxvEgIgG1Uk6zir42m3fX7V3UWVyH3y78hkKmYFrraRx87aD14k8TC2tHwfrRJvFXoQ28eVCIP4FAkG12797NiBEjCAgIYOjQoXh6erJx40YePHiQ4zltYgHUaDTMnz+fL7/8kuDgYFtMWaAxu4BtURxXLpdR3F3Nk2gNoTEaSng5vtxGViQ42gKYtG58blzASefgLCyAGeKblAUc5uAbkz1J7t+mFXxt0ns6UZ/Ihzs/5Jsj3wBQyacSq3uvpkmZJtZP8uQi/DUUwq6BTA5tPoSWEyGL2mgCgUDwLKVLlyY8PJzOnTvzww8/0L1791T1jXOK1d+WGo2G6dOns337dtRqNZMnT6ZXr14sX76cjz76CIVCwYQJE3K9ocKAxoYWQAB/D2eeRGsIiUkEvGwypz1JcHAMoIvFAqjP8RwiBjBrzGVgHJ0FbHb/tq3ql+u5zgSfYeCagVwMvQjAGw3e4KsXvsJdbWViiSTBqZWmZA99IniUNLl8g57P9d4EAkHRZPr06fTt2xdvb2+bzmu1AJw6dSpLliyhQ4cOHDp0iL59+zJs2DCOHDnC3Llz6du3b6oOHUWZ5Cxg21yPgpYJ7GjrmYsNysCILOCs8XFLcgE7MAs4JlHH8TvhQO7q/xmMBr469BVTdk9BZ9QR4BbATz1+omuVrtZPookxlXe58LfpcaUO0HsJuBXP8b4EAoFgxIgRANy4cYObN2/SqlUrXFxckCQpV0lvVgvAv/76i5UrV9KjRw8uXLhAnTp10Ov1nD171i5ZdwUZW2YBQ4pagAVEAJpdr64OSwKxXQygsABmjNkFHKc1kKgzOEQsH7zxFJ1BonxxN4KKu+VojjuRdxi8djD77+0HoFe1XvzQ7Qf83LJhUXx8zuTyDb8JMgW0nwLN3wa5iCEVCAS54+nTp7zyyivs3r0bmUzG9evXqVChAq+//jrFihXj66+/ztG8Vn87PXjwgAYNGgBQq1YtnJycmDBhghB/6WApBG0rF3ABKwWT6OhWcDboBJIgOoFkiYeTEnVSVrqjSsHsSVH+JbtIksTPZ36mzqI67L+3H3e1O8t6LGPNK2usF3+SBMeXmrp6hN8EzzIw7D94foIQfwKBwCZMmDABlUrFvXv3cHV1tTzfr18/tmzZkuN5rbYAGgyGVP12lUol7u72Kbha0LG4gG1lASxgLmBzGRhHuU8tvYB1hhybyBNFJ5Askclk+LipCY5O5GmshtLeLnm6viRJOW7/FhYfxqiNo/jn8j8AtAhswcreK6lQrIL1kyRGwYZxcGmd6XGVF6HXQnD1ydZeBAKBIDO2bdvG1q1bKVOmTKrnK1euzN27d3M8r9UCUJIkhg4dask8SUxMZNSoUbi5pXa7rFmzJsebKSzYsg4gFLx2cMlJII4pM2m2AEqS6b3IiYgTSSDW4etuFoB5bwG8/DiGJ9EaXFQKGpe3XnRtubGFYeuHERwbjFKuZGabmUxuMRlFdjJ0H56Cv4dBxB2QK6HDDGg2BoRHRCAQ2Ji4uLhUlj8z4eHhucoGtvoXesiQIakeDxw4MMeLFnZsHQOY7AJOtMl89sbRZWBSrpugzVlsmkYkgVhFci3AvBeAZutfi0q+Vr1P8bp4Jm+fzILjCwCoXrw6q/uspn7J+tYvKklwdAls+xiMOvAqC32XQ5mGOToHgUAgyIqWLVuycuVKZs2aBZi8L0ajkS+++IK2bdvmeF6rBeDy5ctzvEhRQpKk5ELQNoof83M31f4LjdHkOusnL3B0IWilQo5aIUdrMBKvM1AsB3M4+hwKCpZ2cA6wTifH/2Xt/j3+8DgD1w7k2tNrAIxrPI7POnyGiyobbuuECFg/Fq5sND2u1g16zgeXnHzCBAKBwDq++OIL2rdvz4kTJ9BqtUyePJmLFy8SHh7OwYMHczyvaAVnY/RGCaNk+n8nG5XFMccAJuqMxGr0eDirbDKvvXB0KzgwCTdtgjHHiSCOtmIWFBzVDzgqXsfJuxEAtM2k/ZveqGfO/jnM3DcTvVFPKY9SrOi5go4VO2ZvwQcn4K9hEHUPFGp4YTY0HilcvgKBwO7UqlWLa9euMX/+fDw8PIiNjaVPnz6MGTOGkiVz3k9cCEAbY47/A9tZAF3UCjyclMRo9ITEaPK9AEx0cCFo89pRCbocCUBJkkg0u/FFFnCmWFzAeRwDePT2U4wSVPJ3zzD55PrT6wxaO4ijD48C0K9mPxZ2XYiPSzaSNIxGOLIAdkwHox6KBUHfFVCqXq7PQSAQCLJCp9PRuXNnFi9ezEcffWTTuYUAtDGaFMWHzSUybIGfhxMxGj2hMRoq+uXf7GtJkohP6sDhSPeppRRMDopBa/RGJCn1PIL0SbYA5q0L+NhtU/HnJukkf0iSxI+nfmTC1gnE6+LxcvJiYdeF/K/W/7IXPhEfburle32r6XHN3tD9W3DO/914BAJB4UClUnHu3Dm7zC3MGzbGbAFUK+TI5bZzDxUvIKVgtAajxQXuUAFoLgWTg3ZwiSlEo0gCyRxzMei8tgAeS+r+8Wz275PYJ/T4vQdvbHyDeF08bYPacu7Nc/Sv3T974u/eEVj8vEn8KZyg61x4ebkQfwKBIM8ZOHAgP/30k83nFRZAG6O1cR9gM/4eBaMYdEqXq0NjAHNRDNrcBk4pl6GyoRW3MJLsAs67z2WsRs+Fh1FAagG44eoGhm8YTmh8KGqFmjnt5zC+6Xjksmy8h0YjHJwHu2aDZADfSiaXb4natj0JgUAgsBK9Xs+yZcvYsWMHDRo0SFN+b+7cuTma1yoBuGHDBqsn7NGjR442UliwWABtLAALSjFos8tVpXCseMpNP2BRA9B6UiaB5FWG+qm7ERglCPRxoaSXCzGaGCZsncBPp013yHUC6rC692pqB2RTtMWGwto34OZO0+Par0C3ueDkYeMzEAgEAuu5cOEC9eubylVdu3Yt1Wt27wXcq1cvqyaTyWQYDDlvv1UYsHUNQDMFRgA6uAuIGVeLCzgnFkBzAogQgFlhdgFr9EbitAbcnezvVDDH/zUO8uXQ/UMMWjuIWxG3kCFjUvNJzGo7CydlNouj3jkAf78OscGgdIEuX0C9QSLLVyAQOJzdu3fbZV6rvq2NRmPWgwRAii4gNhYP/h6mWoD5vRi0WXA5MgMYkq13ibmxAKqF+zcrXNVKXFQKEnQGnsZq8lQAPkzcS8vlEzBKRsp6lWVlr5W0DmqdvcmMBtj/NeyZA5IRilc1uXwDath+4wKBQJCPEDGANsZeMYAFxQKYmE/cpy5JbehyZAEUNQCzhY+bmoeRCTyN01LO1y3rA3JBos7AqfsmAfjPzc8xyo0MqjOI71/8Hq/sJmjEPIE1I+D2XtPj5wZAly9Bbd9zEAgEgvxAjgRgXFwce/fu5d69e2i1qbP/xo0bZ5ONFVTMLmCbxwAmBduH5fN+wPH5xAVsFm85EoD6/HEOBYXi7kkC0M6ZwEbJyLRty9AbSqEnHE/XRBZ3+5O+Nftmf7Jbe/7f3n2HN1W9ARz/JmnTXdoCpYAICMiQDYKoLEXKXk5kgwgCioAIOJjKUpAhWtk4EEWBnwIiiExBFKQigshGRqFAS3fm/f0Rkra0tEmapmn7fp6nz8NNzj333N62eTnjPfDtYEi+Bt7+llW+9Xu6vM1CCOGpHA4ADx8+TIcOHUhJSSE5OZmwsDCuX7+Ov78/4eHhEgAa8rcH8EayHqPJjJeHrk5N9YAk0Bmv79QQsF72AXaEO1YCX0q4xMDvBvLrPyGE0pfQ4Ov8NuwvygWVc6wikxF2zYLd7wEKhNeyDPmWrp4fzRZCCI/lcBQxatQoOnfuTFxcHH5+fvz666+cP3+eRo0a8f777+dHGwsV2xxAL9cGD2EBWjRqFYri/m23HGHbQq2g5wDmIQ+grAJ2TFg+bwe39u+11Pm4DltPb8VfqQvAmJbdHA/+Eq7Ap11g92xAgYb9YPDPEvwJITxOw4YNiYuzbHc5depUUlJSXH4NhwPA6OhoxowZg1qtRqPRoNPpqFChArNnz+aNN95weQMLm/xaBaxRq2wpNzx5HmB68FSw00vTdwJxfAGTtdfQV7aBs0t+JYOOT4unz/o+PPPNM8SlxdEw4kFC1JYt2JreV9Kxyk7+BFGPwPlfQBsIPZZClwXgnf02ckKIomH37t107tyZcuXKoVKp2LBhQ6b3+/fvj0qlyvTVrl27TGVu3rxJr169CA4OJiQkhEGDBpGUlJSpzJEjR2jevDm+vr62mOhOa9eupUaNGvj6+lKnTh02b95813YfP36c5ORkAKZMmZLleq7g8Ke0t7c3arXlgzE8PJwLFy5Qs2ZNSpQowX///efyBhY2tkUg+RA8lA7y4VqizqMDwBQP6QG0DgGn5mEnEOkBtE+pAOv0BNf9XO48t5O+6/vyX8J/qFVq3nj0DbpVeYUnP/6NYF8vqpexMzefyQg73oG9H1iOy9SxDPmWquqytgohPFdycjL16tVj4MCB9OjRI9sy7dq1Y8WKFbZjH5/MaaR69erFlStX2LZtGwaDgQEDBvDiiy+yevVqABISEmjbti1t2rQhKiqKv/76i4EDBxISEsKLL74IwL59++jZsyczZsygU6dOrF69mm7duvHHH39Qu3btLG2qX78+AwYM4NFHH0VRFN5//30CA7PfBnbixIlOfW8cDgAbNGjA77//TrVq1WjZsiUTJ07k+vXrfPbZZ9neRHGTcSs4VwsP8uFvPDsVjDV48i/oRSB5yAPoKcPYhYW1B/CmC4aAdUYdb/78JnP3z0VBoUpoFT7r/hnNKjRj6Z4zgGX3D7u2Wbx10ZLb779fLccPvgBt3wVv3zy3UwhROLRv35727dvnWMbHx4eIiIhs3zt+/Dhbtmzh999/p3HjxgAsXLiQDh068P7771OuXDm++OIL9Ho9y5cvR6vV8sADDxAdHc3cuXNtAeD8+fNp164dY8eOBWDatGls27aNDz/8kKioqCzXXblyJZMmTWLjxo2oVCp++OEHvLyyhmwqlcp9AeD06dNJTEwE4N1336Vv37689NJLVKtWjeXLlzvViKIkv+YAQuFIBWOdc1fQwVP6ELDzq4Dz4xkWRdY5gNfzOAR85OoReq/rzV/X/gLghQYv8EG7DwjUWv7Xe+B2/r8HK4XdtQ6bE1tgw1BIjQOfYMtw7wPd89Q+IYRnSExMJCEhwXbs4+OTpdfOETt37iQ8PJzQ0FAee+wx3nnnHUqWtEwz2b9/PyEhIbbgD6BNmzao1WoOHDhA9+7d2b9/Py1atECr1drKREZGMmvWLOLi4ggNDWX//v2MHj0603UjIyOzDElbVa9enTVr1gCgVqvZvn074eHhTt9jdhwOADN+E8LDw9myZYtLG1TY6Wy7SOTPEDDA2espXIxLsb3mSYGKdQVtgQeAtiFg51cBF/Q9FBal8rgK2KyYmbt/Lm/+/CZ6k57S/qVZ2mUpXaqnbytpNiscPHd7B5DKOQSARj1snwL7P7Qcl60PT6+AsPucapsQwvPUqpU5UfukSZOYPHmyU3W1a9eOHj16ULlyZU6fPs0bb7xB+/bt2b9/PxqNhpiYmCyBl5eXF2FhYcTExAAQExND5cqVM5UpU6aM7b3Q0FBiYmJsr2UsY60jJ/m1GYckgnYxXT4lgob03UC+/eMi3/5xEYDyIX78/FpLjwkCPWUFrX8e9gK25QH0kO+pp8s4BOzofsDn48/T/3/92XluJwCd7u/E0s5LKROY+Q/lqdgk4lIM+HlrqF3+Lgmf487DNwPh0kHLcdOX4Ikp4Oi2cEIIj3bs2DHKly9vO85L799zzz1n+3edOnWoW7cuVapUYefOnTz++ON5aqcrnT59mnnz5nH8+HHAEgSPHDmSKlWqOF2nwwFg5cqVc/wDf+bMGacbUxTY5gDmQwDY4v7SlA/xsyWD1hnNXIpP5b+bqVQNz35yqLtZF10UdB5A6yrkPO0EIlvB2cU6BGw0KySkGinh753rOYqi8PmRzxnxwwgSdAkEeAfwQeQHvNDwhWz/vliHfxtVDMU7u/m1xzfC/4ZB2i3wLQFdP4KanfJ2Y0IIjxQUFERwcHC+1H3fffdRqlQpTp06xeOPP05ERATXrl3LVMZoNHLz5k3bvMGIiAiuXr2aqYz1OLcyd5t7mNGPP/5Ily5dqF+/Po888ggAv/zyCw888ADff/89TzzxhFP36nAA+Oqrr2Y6NhgMHD58mC1bttgmNxZn+TkHsHKpAH4Z/5jt+LH3d3LmejKxiTrPCQANnrGLhnX4Ns2ZIWAP6cUsLHy8NAT5eJGoM3I9WZdrAHgj5QYvbXqJtcfWAvDQPQ/xWffPqBp295W5v91t/p9RB9smwoHbk6jLN4anlkNoRedvSAhRbF28eJEbN25QtmxZAJo1a0Z8fDyHDh2iUaNGAPz888+YzWaaNm1qK/Pmm29iMBjw9rb8/du2bRvVq1cnNDTUVmb79u2ZYqht27bRrFmzXNs0fvx4Ro0axcyZM7O8Pm7cOPcFgCNHjsz29UWLFnHw4EGnGlGU5FcewOyUCvKxBIAetD2ctcetoHsArddPMZgcHpZMs83jlADQXiUDtSTqjNxM1lOl9N3LbT29lf4b+nMl6Qpeai8mtZzE+EfH46W++58iRVH4/Ww28/9unoG1A+BKtOW42Qh4fBJ4abNWIoQolpKSkjh16pTt+OzZs0RHRxMWFkZYWBhTpkzhySefJCIigtOnT/P6669TtWpVIiMjAahZsybt2rVj8ODBREVFYTAYGDFiBM899xzlylmS0T///PNMmTKFQYMGMW7cOI4ePcr8+fP54IMPbNcdOXIkLVu2ZM6cOXTs2JE1a9Zw8OBBFi9enOs9HD9+nK+//jrL6wMHDmTevHlOf29cFqW0b9+eb7/91lXVFVr5OQfwTuEeuCrYU3LoWXsgTWYFvcmxCbTSA+g4224gd/nPSIohhZc3v0zk55FcSbpC9ZLV2T9oP2+1eCvH4A/gv5upxCSk4a1R0eDeEMuLf6+HT1pagj+/UOj5FUS+K8GfECKTgwcP0qBBAxo0sCSRHz16NA0aNGDixIloNBqOHDlCly5duP/++xk0aBCNGjViz549meYVfvHFF9SoUYPHH3+cDh068Oijj2YK3EqUKMHWrVs5e/YsjRo1YsyYMUycONGWAgbg4YcfZvXq1SxevJh69erxzTffsGHDBrvS55UuXZro6Ogsr0dHR+dpZbDLFoF88803hIXZkZ6hiEtPBJ3/wYMnpoWx9gD6ekgPIECa3uzQkLx19xAJAO1n3Q84u1Qwhy4fovf63vxz/R8Ahj84nNlPzMbf29+uug+cvQFA3XtC8MUAG8fCwWWWNys8BE8tgxL3uOAuhBBFTatWrVAU5a7v//jjj7nWERYWZkv6fDd169Zlz549OZZ5+umnefrpp3O93p0GDx7Miy++yJkzZ3j44YcByxzAWbNmZUkt4winEkFnHE5TFIWYmBhiY2P56KOPnG5IUZGfiaDvZA0APSkxdKqHJIL21qjx1qgwmBRSDEZKkPvCBCudh8xjLExKZZMM2mg2MnPvTKbsmoLRbKRsYFmWd11Ou6rt7lZNtqzz/yIjkmBpG7hqyRPIo6Oh9Rugsf/ZCiFEYfP2228TFBTEnDlzmDBhAgDlypVj8uTJvPLKK07X63AA2LVr10wBoFqtpnTp0rRq1YoaNWo43ZCiIj/zAN6pdKDn9QB60i4avt4aDCajw7kAbUPAsgrYbncOAZ++eZo+6/uw/+J+AJ6s+SSfdPqEkv4O7uEL/H7uJl3UvzDw75VgTAb/UtDjE6jaxmXtF0IIT6VSqRg1ahSjRo2ybcQRFGTndpg5cDgAdDbZYnGRn6uA7+SJQ8C2HkAPCAD9tRoS04wOp4KxBozSA2i/kgHpQ8BL/1jKq1teJdmQTJA2iA87fEifun0cWohjdfXGTYbcmk9P7Q4wAhUfhSeXQnBZF9+BEEJ4PlcEflYOB4AajYYrV65kmXh448YNwsPDMZkcT7tRlLhzEYg1ALzugauAPSF4cnY7uDQZAnaYNRn0rrMH+ejUEACa39ucT7t/SqWQSs5VGnsCv8960dPrJGZUqFu+Di1eB43krxdCiLxyOEq522RKnU6XaR+84kp/Ow1MfiSCvpM1ALyRrMfo4ErX/GAyK7ZFMP7agv+Q9rvdBkeHgNNkEYjD/o37A4CbyUa81d7MajOLHf12OB/8Ra+Gxa0ITjhJrFKCz6vNvz3fr+B/roQQoiiw+6/pggULAMtY9NKlSwkMTE88bDKZ2L17t8wBxL09gCUDfFCrwKxYJt+HB/vm+zVzkrGnzROCJ7/b8zAdGQLOmDZGegBzl6RPYvSPo1l5cBvl+BAfVUl+H/w79SLqOVehPhk2vQZ/WlbcHfaqx4tJQ5hW77FcThRCCOEIuwNAa0JDRVGIiopCo0n/cNRqtVSqVImoqCjXt7CQceccQI1aRclAH2ITdVxL1BV8AJgh0PJ1wyKY3Fh7IdMcGAJO87Ag1pP9evFX+qzvw6mbp1CrQgBQKYHUDq/rXIVXj8HafnD9X1CpSX3kdZ78qTZm1Fl3ABFCiGLAYDDQrl07oqKiqFatmkvrtjsAPHv2LACtW7dm3bp1tu1NRGbuXAUMlpXAsYk6j9gNJGMSaGcm/LuadSWyIz2AGXsx3dGLWxgZTAam7Z7Gu3vexayYqRBcgaWdV/Di0jTMCsSn6G15Ae2iKPDHp/DD62BMg6Cy8ORS9qZUw8xBqpQOcKw+IYQoIry9vTly5Ei+1O3wJ9yOHTsk+MuBdfjQXcGDbSVwQsEHgJ6yDZyVtQcvRW+0+xzbNnBeatTqgg9iPc2J6yd4ePnDTNs9DbNipledXhx56Qhtqz5OyO09gG8kZ00GfVe6RFg3GL5/xRL8VXkchu6FSo/y+znr9m+Op44RQoiionfv3ixbtszl9To8o/rJJ5+kSZMmjBs3LtPrs2fP5vfff2ft2rUua1xhoyhKeiJodweAHtADmOphq2etgagzQ8CekMfQkyiKwke/f8TYbWNJNaYS4htCVMconq39rK1MyQAt8SkGbiTpoYwdlV45At8MgBunQKWBx96CR14FteV358DtBNBNK8vwrxCi+DIajSxfvpyffvqJRo0aERAQkOn9uXPnOlWvwwHg7t27s80F2L59e+bMmeNUI4oKg0nBukjaHXMAwbNyAVp72jylB9DX24khYL2sAL7TlcQrDPxuIFtObQGgzX1tWNF1BfcEZ95+rWSAD6djk7mRnMvPoqJYtnLb8gaYdBBcHp5aDvc+ZCuSrDNy9NItAB6UAFAIlzCbzcTHx9tVNiQkJF/bIux39OhRGjZsCMC///6b6b28TLdyOABMSkrKNt2Lt7c3CQkJTjekKNAZ3T9/zJN2A/G03jNrIOpIHsA0o2f1Yha0b499y4sbX+Rm6k18ND7MfmI2I5qMQK3K+vNtzQV4I5v9gG3SbsH3I+Hv9Zbj+9tBt4/BP3OQ98eFOExmhfIhfpQP8XPZ/QhRnMXHxzN342F8A3JOJpyWnMjoTg3c1CqRmx07duRLvQ4HgHXq1OGrr75i4sSJmV5fs2YNtWrVclnDCiPr8C+4LwAMD/acANCTkkBDhgDQoR5Az7qHgnIr7RavbHmFT//8FIAGEQ34vMfn1Cp9999xWwB4tzmAlw/D2v4Qdw7UXtBmCjQbDtn8D/Z3Gf4VIl/4BgQREBxS0M0QTjh16hSnT5+mRYsW+Pn5oSiKe3sA3377bXr06MHp06d57DFLbq7t27fz5ZdfFuv5f4AtCbLWS+22VbC2HkBPmAPoYYtAfJ3YCSR9HmPxXQG858IeBn4/kPO3zqNWqRn3yDgmt5qMVpNzovew29vB3bjzZ1FR4LfFsPUtMOmhxL3w9Aq4p/Fd67LO/2siAaAQopi7ceMGzzzzDDt27EClUnHy5Enuu+8+Bg0aRGhoqNPT7xz+lOvcuTMbNmzg1KlTDBs2jDFjxnDx4kV++uknunXr5lQjigpbDkCN+4IHT5oDmJohDYwnsOYBdGQOYJqH3YM76Yw6Vl1eRZvP23D+1nkqh1RmV/9dTH98eq7BH0Cp7IaAU+Pgq96WFC8mPdToBEN35xj86YwmDv8XD8j8PyGEGDVqFN7e3ly4cAF/f3/b688++yxbtmxxul6n9lXq2LEjHTt2zPL60aNHqV27ttONKeyscwDdlQMQ0gPAJJ2RFL2xQLdgs/YAesocQD+t5Tk4MgRcXAPAo9eO0uvbXhy5Zsk3NbD+QD5o9wHBPsF211Hydg/gTesQ8MWDsHYA3LoAam9o+w40HZLtkG9GRy7eQm80UypQy32lAnIsK4QQRd3WrVv58ccfueeezAvvqlWrxvnz552uN8+RSmJiIosXL6ZJkybUq+fk9k9FhM7gvl1ArAJ9vGzDlQXdC2jtafOU4MnP+/ZewA6lgSle28CZFTNz98+l0eJGHLl2hGBNMF8/+TXLui5zKPiD9DmA15PSYN9CWB5pCf5CK8GgrfDQ0FyDP4DfMgz/ekJCcSGEKEjJycmZev6sbt68iY+P80nynQ4Ad+/eTd++fSlbtizvv/8+jz32GL/++qvTDSkK3J0EGixLwD1lGNjae+YpcwDzshNIcQgA/7v1H20+bcOYrWPQm/S0r9Ke+TXm0616N6fqKxmgJYREJiVNs8z3MxuhVjcYshvKN7S7HlsAKNu/CSEEzZs359NPP7Udq1QqzGYzs2fPpnXr1k7X69B4YUxMDCtXrmTZsmUkJCTwzDPPoNPp2LBhQ7FfAQzpPYDuSgJtVTrQh/9uphZ4AOh5cwAdTwSdPoxdtBeBrP5rNcM2DeOW7hb+3v7MaTuHgXUH8sMPPzhdZ5n4aDb5vEF5bqBofFC1mw6NB9nV62dlNJk5dD4OkPl/onhxNEefWl20/0aJdLNnz+bxxx/n4MGD6PV6Xn/9df7++29u3rzJL7/84nS9dgeAnTt3Zvfu3XTs2JF58+bRrl07NBoNUVFRTl+8qLHNAXRzABge5AsU/Epg2xBwAc5DzMipreCseQDdOIzvTnGpcQzbPIw1R9cA0KR8Ez7r/hn3l7wfg8HgXKVmM/wyj6Cf3yFYZeKMOYKQ3l8QVvXuCz3u5viVRJJ0RoJ8vagR4dgQtBCFmaM5+sLC5D9IxUXt2rX5999/+fDDDwkKCiIpKYkePXowfPhwypYt63S9dn9S//DDD7zyyiu89NJLVKtWzekLFmW2VcBuDh48ZQg4vQfQM/5n6swQcJqHLWRxpZ/O/ET/Df25lHgJjUrD2y3e5o3mb+Ct8Xa+0uTrsH4InPoJFfCD6lFe0w9grf/9OPPx9Nvt/X8frBSGRvZiFsWM5OgTd1OiRAnefPNNl9ZpdwC4d+9eli1bRqNGjahZsyZ9+vThueeec2ljCruCWAUMHhQA2vIAekYPoFNDwEVwDmCqIZUJ2ycw/8B8AKqFVeOz7p/R9J6meav43C/w7SBIvAJevtDhPebtrEhyalLu28HdxW9nbwCWAFAIIYRFXFwcy5Yt4/jx4wDUqlWLAQMG5Kkn2O5I5aGHHmLJkiVcuXKFIUOGsGbNGsqVK4fZbGbbtm0kJiY63YiiQm90/yIQ8LwA0NdDes+sQ8AGk4LBZM6ltEVRWwV8+MphGi9pbAv+hjQawuEhh/MW/JlNsOs9WNXJEvyVuh8G74CGfSkZdEcqGAcoipJpBbAQQgjLottKlSqxYMEC4uLiiIuLY8GCBVSuXJndu3c7Xa/DkUpAQAADBw5k7969/PXXX4wZM4aZM2cSHh5Oly5dnG5IUaAzFtwiEIBrBZ0GxroK2EOCp4zDuPamgvG0hSzOMplNzNw7k6ZLm3Is9hhlAsqw6flNRHWKIkCbh9x6iVfhs+6w4x1QzFDveXhxJ5SxLAILC7CmgnE8ADx1LYm4FAO+3mrqlC/hfBuFEKIIGT58OM8++yxnz55l3bp1rFu3jjNnzvDcc88xfPhwp+vNU6RSvXp1Zs+ezcWLF/nyyy/zUlWRUBB5AMFzegA9bf6cVqO2zSOzNxl0WhHYCu5s3FlarWrFhO0TMJgNdKvRjb9e+osO1TrkreIzOyHqUTi7C7z9odvH0P1jyBBQlgq8y3ZwdrDO/2t4b6jb/xMlhBCe6tSpU4wZMwaNJv2zVaPRMHr0aE6dOuV0vS6ZrKXRaOjWrZtsBVdAq4CtAeD1JB1ms4K6gCbPpxgsq209ZfhUpVLh560hSWd0OAAsjD2AiqKw6s9VvPLDKyTqEwnUBrKg3QL61++ft4TKZhPsmgW7ZgMKhNeCp1ZAeI0sRUve7gF0ZgjYOvwr8/+EECJdw4YNOX78ONWrV8/0+vHjx/O0AYdnzNYvIgpqDqC118VoVohPNdiG4dwtVW+5f09JBA2W3kjLNnmODQF7yjxGe11Puc6QjUNYd3wdAI/e+yifdvuUyqGV81ZxwhX49gU4v9dy3LAvtJsF2qxZ6QHCAp0bAs44/6+pzP8TQhRzR44csf37lVdeYeTIkZw6dYqHHnoIgF9//ZVFixYxc+ZMp68hAaALFdQcQK2XmlB/b+JSDMQm6gowALT0AHpS75m1LakG+3IB2haBFKI8gJtPbmbg/wZyNfkq3mpvpraeytiHx6JR5/EeTv0E64ZAynXQBkKneVD36RxPse4H7Ogq4ItxqVy5lYaXWkWDe0OdbbEQxYq9yaMlcXThU79+fVQqFYqi2F57/fXXs5R7/vnnefbZZ526hkcEgIsWLeK9994jJiaGevXqsXDhQpo0aZLreWvWrKFnz5507dqVDRs25H9Dc1FQeQDBMgxsDQCrR+ScSDQ/KIpi6z3zpB5Aa1usvZO5SfWweYw5SdYnM3bbWD4++DEAtUrX4vPun9OgbIO8VWw2wk/vwt4PLMdl6sDTK6FU1VxPLRXo3BCwtfev7j0lCsX3XghPYE/yaEkcXTidPXs2369R4AHgV199xejRo4mKiqJp06bMmzePyMhITpw4QXh4+F3PO3fuHK+99hrNmzd3Y2tzVlBzAMESAP57NYnYpDS3Xxsswa/59n9UPGn4ND0ZtL09gIVjDuBvl36jz/o+/HvjXwBebfoq0x+fjp+3X57q9dXfQPNZV7h4wPJC40EQOR28fe0639r7fMPBIWDb/D8Z/hXCIZI8umiqWLFivl+jwAPAuXPnMnjwYAYMGABAVFQUmzZtYvny5YwfPz7bc0wmE7169WLKlCns2bPH7v0T85ttFXABrCC1pYJJKJiVwBmTLXtS8JQ+BFw0VgEbzUbe3f0u03ZPw6SYKB9UnpXdVtLmvjZ5rlt1ciut/3kLtSkZfIKh83yo3cOhOkre/jlM0hlJM5jsXhBkXQEs8/+EECKry5cvs3fvXq5du4bZnHlE65VXXnGqzgINAPV6PYcOHWLChAm219RqNW3atGH//v13PW/q1KmEh4czaNAg9uzZk+M1dDodOl16UGRNWG0wGJzf+/Qu0m7PM/NS4fK6c1MywLKd19VbqW6/NkBCiuV77K1RgdmEwZx7wGVtZ3621/d2b2xSqj7X62QcxvZSKQXyfczJyZsn6f9df36//DsAz9R6hoWRCwn1C81bW00G1DvfwevXRZbDMnUxP7kMQiuDg/X6aRS8NSoMJoVrt1IoWyL3nsM/L97i7PVkVCqoVy7I477vBcEdvxvCPu56FgaDAUUxYc7lb6eimGyfX/ac42j5jOdY/+3qNt15jr2K6+/DypUrGTJkCFqtlpIlS2bK6qBSqQpnAHj9+nVMJhNlypTJ9HqZMmX4559/sj3HuiVddHS0XdeYMWMGU6ZMyfL67t27OXbsmMNtzsmFS2pAzYnjf7P55lGX1p2b65dVgIY//z3LZuW0W68NcDUVwAsvzGzevNmhc7dt25YvbQKIu255Jgf//IvAa0dyLGs0g1mx/Ers2bEdvwLvH7dQFIWtN7ay/PJydGYd/mp/hlYYSgttC/bvuPt/lOzhp79O47OLCEux/MycLt2WYxHPYt5/HDjuVJ3+ag23TCq++/FnKgTmXPZGGnxwVAOoqBtqZu+O/PtZKIzy83dDOCa/n0ViYiKnL6lznM8Hljl929JOExQUZNc5jpbPeA6QL2268xx7Xb9+3e6yRcnbb7/NxIkTmTBhgksX83jIR5x9EhMT6dOnD0uWLKFUqVJ2nTNhwgRGjx5tO7506RK1atWiRYsWVKpUyaXtWxt7COJu0LhBPTrUL+fSunNjiL7M/84fRRtcig4dGrv12gB/X06A6F8J9velQ4eWdp1jMBjYtm0bTzzxBN7e3vnSrj26vzl84xL3Va1Oh5b35Vg2IdUAB3YA0LlDO49IRhyTFMOQTUP44eIPALSu2JqlnZdSIbhCnutWndiMZuMUVGm3UHxLoG83l6PnvfP8PKLO7udWTCI16j9Iy/tL37VcQqqBZ5b8RqIhmRoRQax84UECfQrVn6R8447fDWEfZ5+FvSt0wbJKNz4+nrN7zuAfFJJj2ZTEeJ5ofh9hYWHcvHkz13McLZ/xHCBf2nTnOfY6d+6c3WWLkpSUFJ577jmXr+Qu0L+2pUqVQqPRcPXq1UyvX716lYiIiCzlT58+zblz5+jcubPtNetYuJeXFydOnKBKlSqZzvHx8cHHx8d2nJCQAIC3t7fL/7DqTZZVEP4+Wrf/0S4batmN4XqSvkA+MAyKpUva38fL4evnx7OwCrgdUOhM5HoNY6plyEKjVuHvq81b8mQX2PDPBgZ/P5jrKdfx0fgw4/EZjHxoJGpVHv8IGHWwbRIcsKwepnwjVE+tQB1YDs5vzvPzKBXkAzGJ3Eoz37UevdHMiDWHOB2bTESwLysGPEhoYN4WsBRF+fm7IRzj6LO4efMmC388aldP2OhODfD29kal0qDOJX2TSqWxtcWecxwtn/Ec679d3aY7z7FXcf1dGDRoEGvXrr3rughnFWgAqNVqadSoEdu3b7ftImI2m9m+fTsjRozIUr5GjRr89ddfmV576623SExMZP78+VSokPdekbzQFVAiaMiwHZwTW3C5gjV9iqfsAmLlp7X8iNuzCMS2AMRLXaDBX6IukZFbRrIiegUA9crU4/Men1M7vHbeK795Ftb2hyvRluNmI+DxSeCldXi+391YdwO5Wy5ARVGYsO4v9p+5QYBWw/L+D1K2hAR/ouiRFbrCFWbMmEGnTp3YsmULderUyRIIz50716l6C3y8ZfTo0fTr14/GjRvTpEkT5s2bR3Jysm1VcN++fSlfvjwzZszA19eX2rUzfwiGhIQAZHm9INh2AinAVcDxKQZ0RpPbcxFad9rwpByAkN4ee3YCsQaJBZmHbu+FvfRd35ez8WdRoWLsw2OZ2noqPl4+uZ+cm783wHcvgy4B/EIte/lWb5/3eu9Q0rYfcPapYBZsP8W3f1xEo1axqFdDapULdnkbhBCiqJgxYwY//vijbSu4OxeBOKvAA8Bnn32W2NhYJk6cSExMDPXr12fLli22hSEXLlwoNBnMrXkAtRr3t7eEn7dt9eWNJD3lQtzbo+Kp+fNsaWDsyANYkL2YepOeyTsnM+uXWZgVMxVLVOTT7p/SomKLvFduSIOtb8LvSy3HFZrCU8uhxD15rzsbJQOtPYBZA8BvD13kg58suQunda1Nq+p3z/UphBAC5syZw/Lly+nfv79L6y3wABBgxIgR2Q75AuzcuTPHc1euXOn6BjkpPQ+g+wMItVpFqUAfrtxK41qizu0BYIrHDgHbnwfQtg2cm+/h72t/03t9b6JjogHoW68vC9otoIRvibxXfuM0rO0HMbenTjw6Clq/CZr8m0tTyrod3B3TEfadvs74dZaV2ENbVuH5pvfmWxuEEKKo8PHx4ZFHHnF5vYWja62QKMg5gJBhHmCi++cBeuI2cJDeA2jPELC7ezHNipn5v86n0eJGRMdEU9KvJN88/Q2ruq1yTfD31zfwSQtL8OdfEnp9C20m52vwBxl2A8nQA3jyaiJDPjuEwaTQsW5ZXo+snq9tEEKIomLkyJEsXLjQ5fV6RA9gUaEvwK3gIH0eYIEEgLeHWD1tCNgakKbZ0QOY6sYA8GLCRQb8bwA/nfkJgHZV27G8y3LKBpXNe+WGVPhhHPyxynJc8RF4cikEuyc1kW0I+PYcwGuJafRf8TuJaUYaVQxlztP1UKsLdoW1EEIUFr/99hs///wzGzdu5IEHHsiyCGTdunVO1SsBoAtZewALKn9ceHDB9wAW5AKK7Pg5sAjEGiTm9yKer45+xdBNQ4lPi8fPy4/3277PS41fcs3K49h/LUO+144BKmgxFlqOA437ftVLWReBJOtI0Rt5YdVBLsWnUqmkP0v6Nva4aQJCCOHJQkJC6NHDsW057SEBoIsoipJhCLhgPuBsPYBJaW6/tjXA8rgA0IG9gPO7BzA+LZ7hm4ez+q/VADQu15jPu39O9VIuGg6N/hI2jQZDCgSEQ4/FUKW1a+p2gHUIOM1gZujnf3Dk4i1C/b1ZMaCJ7T0hhBD2WbFiRb7UKwGgi+hN6ZszF0QaGCjYOYDW3jN/D+vd8bfmAbQnDUw+BrE/n/2Zfhv6cTHhImqVmjebv8nbLd7G2xXz8fTJsHksRH9hOa7cAnoshaAyOZ+XT/y1Gny91aQZzOz+Nxatl5olfRtTuVRAgbRHCCFEVhIAuoi19w+K6SIQT+0B1FqehT1DwNZn6OvCHtw0YxpvbH+DD379AICqYVX5rPtnPHTPQ665wNVjlsTO10+ASg2tJkDzMZBLFv78pFKpKBngw6X4VADmPF2PxpXs3+5JCCFEusqVK+c4RejMmTNO1SsBoIvoMwSABZEHENIDwGsFEAB67BCwAzuBuDqI/TPmT3qv783Ra0cBeLHhi8yJnEOgNjDvlSsKHP4MNr8OxlQIjLAs9KjcPO91u0CV8EAuxacyrl0NOtdz777YQghRlLz66quZjg0GA4cPH2bLli2MHTvW6XolAHSRjAtACmobsdKBvoClB1BRFLe2w50raB1hbY/eaMZkVtDksPrUVYtATGYTc/bP4a2f38JgNhAeEM6yLsvodH+nPNVro0uEjaPhr68tx1Ueg+6LIbC0a+p3gbnP1OPc9WQaVQwt6KYIIUShNnLkyGxfX7RoEQcPHnS6XskD6CI6Q8GmgIH0HkCd0UyiLvedL1wp1cO3goPcewFdEcSeiz/HY58+xrifxmEwG+hSvQt/vfSX64K/mL9gcStL8KfSWPbx7fWtRwV/YFkJ3LhSWIHuqSyEEEVZ+/bt+fbbb50+X3oAXaSgVwCDZegyyMeLRJ2R2EQdwb75m/A3I2vw5GkpPny81KhUlhHTFL2RQJ+7/8jnJQBUFIXPjnzGiM0jSNQnEuAdwPx28xnYYKBrgiBFgYPLYcsEMOkguDw8uQwqNst73UIIIQqdb775hrAw5+dXSwDoIvoC3gXEqnSQjy0ArFLaBXPN7JTeA+hZP1IqlQo/bw0pelOuK4F1Tm4FdyPlBkM2DuHb45b/iT1c4WE+7fYpVcKqONfoO6UlwPevwN/rLcfVIqHbxxBQ0jX1CyGE8FgNGjTI1JGgKAoxMTHExsby0UcfOV2vZ31aF2IFvQ2cVakgH85cT3b7SmBPnQMIlmHgFL0pX4aAt5zawsD/DeRK0hW81F5MbjmZcY+Ow0vtol+ty4dh7QCIOwtqL8tWbg8NB7XM3hBCiOKgW7dumY7VajWlS5emVatW1KhRw+l65VPERXS3t4ErqF1ArAoqFUz6KmDP+5HytXM/YGsPoa8d8xhTDCmM2DyC9l+050rSFWqUqsGvg37lzRZvuib4UxQ48Aksa2sJ/krcCwO2wMMvS/AnhBB22r17N507d6ZcuXKoVCo2bNiQ6X1FUZg4cSJly5bFz8+PNm3acPLkyUxlbt68Sa9evQgODiYkJIRBgwaRlJSUqcyRI0do3rw5vr6+VKhQgdmzZ2dpy9q1a6lRowa+vr7UqVOHzZs323UPkyZNyvT19ttvM3To0DwFfyABoMtYhw99CrgHLH03kALqAfSwIWDIsB9wLgFg2u0g3jeXIP7Q5UM0WtyIRb8vAuDlJi/zx4t/0KhcIxe0FkiNg696ww+vg0kPNTrB0N1Q4UHX1C+EEMVEcnIy9erVY9GiRdm+P3v2bBYsWEBUVBQHDhwgICCAyMhI0tLSd9Tq1asXf//9N9u2bWPjxo3s3r2bF1980fZ+QkICbdu2pWLFihw6dIj33nuPyZMns3jxYluZffv20bNnTwYNGsThw4fp1q0b3bp14+jRo/l387nwvE/rQspThoBtuQAT3BcAmsyKbQ6kJw4BW4NSe3sA75YH0Gg2MnPvTKbsmoLRbKRcUDlWdF1B2yptXdfYi4fgm/4QfwHU3tD2HWg6BGQ1rRBCOKx9+/a0b98+2/cURWHevHm89dZbdO3aFYBPP/2UMmXKsGHDBp577jmOHz/Oli1b+P3332ncuDEACxcupEOHDrz//vuUK1eOL774Ar1ez/Lly9FqtTzwwANER0czd+5cW6A4f/582rVrZ8vbN23aNLZt28aHH35IVFRUtu1Tq3NPK6dSqTAancv6IQGgi+hNBZ8GBjIMAbuxBzDj3DpPSwMD4Hc7r19KLnMA03KYA3j65mn6rO/D/ov7AXi61tNEdYoizM9FO1woCuxfBD9NArMRQivBUyugfEPX1C+EEEVEYmIiCQkJtmMfHx98fHwcrufs2bPExMTQpk0b22slSpSgadOm7N+/n+eee479+/cTEhJiC/4A2rRpg1qt5sCBA3Tv3p39+/fTokULtNr0vc4jIyOZNWsWcXFxhIaGsn//fkaPHp3p+pGRkVmGpDNav379Xd/bv38/CxYswGw237VMbiQAdBHbEHABB4DhBTAH0NpzplIV/P1nx7oyOdch4GxWASuKwtI/ljLqx1EkG5IJ9gnmw/Yf0rtub9fluEu5CRuGwb8/WI5rdYUuC8G3hGvqF0IID2U2m4mPj7e7LECtWrUyvT5p0iQmT57s8LVjYmIAKFMm877pZcqUsb0XExNDeHh4pve9vLwICwvLVKZy5cpZ6rC+FxoaSkxMTI7XyY61VzKjEydOMH78eL7//nt69erF1KlT7bnVbEkA6CKekAcQCmYRiG3o1FvjkYl//WyLQHLuJr8zl+G15GsM/n4w3534DoBWlVqxqtsq7i1xr+sad+EAfDMQEi6CxgfaTYfGg2TIVwhRLMTHxzN342F8A4JyLJeWnMiTdSwjLseOHaN8+fK295zp/StsLl++zKRJk1i1ahWRkZFER0dTu3btPNUpAaCLWFcBF3QPmDUAvJmsy3XrM1fx5BQwkD6nL9WQc1d5mi0AVPP9ie8Z9N0gYlNi0Wq0vPvYu4xuNhq1ykXP12yGffNh+zRQTBBWBZ5eCWXruqZ+IYQoJHwDgggIDrG7fFBQEMHBwXm+bkREBABXr16lbNmyttevXr1K/fr1bWWuXbuW6Tyj0cjNmzdt50dERHD16tVMZazHuZWxvn83t27dYvr06SxcuJD69euzfft2mjd3zZ7vnjdeV0jZEkHncR/ZvCoZ4INaBWYFbiS7pxfQ2rN2t8UTBc0amKbm0ANoNiu2Xty3drxOlzVdiE2JpU54HX4f/DuvPfya64K/5Ouw+hn4abIl+Kv9FAzZJcGfEEK4UeXKlYmIiGD79u221xISEjhw4ADNmll2WWrWrBnx8fEcOnTIVubnn3/GbDbTtGlTW5ndu3djMBhsZbZt20b16tUJDQ21lcl4HWsZ63WyM3v2bO677z42btzIl19+yb59+1wW/IH0ALqMpwwBa9QqwgJ8uJ6kIzZRR3iQb75f09N7AP1tPYB3nwNoTQED8OmRpahUKsY0G8O0x6bh6+XC7+G5X+DbQZB4Bbx8of0saNhPhnyFECIfJCUlcerUKdvx2bNniY6OJiwsjHvvvZdXX32Vd955h2rVqlG5cmXefvttypUrZ0u+XLNmTdq1a8fgwYOJiorCYDAwYsQInnvuOcqVKwfA888/z5QpUxg0aBDjxo3j6NGjzJ8/nw8++MB23ZEjR9KyZUvmzJlDx44dWbNmDQcPHsyUKuZO48ePx8/Pj6pVq7Jq1SpWrVqVbbl169Y59b2RANBFrAFgQSeCBssw8PUkHdcSdTzghuulbwPnmQGgtWfybmlgDCYDk36eDlhWeVUoEcGq7itpVamV6xphNsGeubBzOihmKHW/Zci3jDuekBBCFE8HDx6kdevWtmPrStx+/fqxcuVKXn/9dZKTk3nxxReJj4/n0UcfZcuWLfj6pv/H/4svvmDEiBE8/vjjqNVqnnzySRYsWGB7v0SJEmzdupXhw4fTqFEjSpUqxcSJEzPlCnz44YdZvXo1b731Fm+88QbVqlVjw4YNOc7j69u3b77Oq5cA0EV0Bs+YAwiWAPD4FfctBLlz8YSnSR8CzhoA/nP9H3qv6030pQvcwwpUKhN/vvQnJVy5AjfpGqwbDGd2Wo7r9YQO74OP+/ZqFkI4z2w2c/PmTby9vXMtGxISglp26/EYrVq1QlGUu76vUqmYOnVqjqtpw8LCWL16dY7XqVu3Lnv27MmxzNNPP83TTz+dc4MzWLlypd1lnSEBoIt4SiJoyLAbiJsCwBQP7wHMbghYURQW/b6IsdvGkmZMo6RPLdBBsK+va4O/M7sswV/SVfD2twR+DXq5rn4hRL5LTk5m/g9/4h8UkmO5tORERndqQFiYi/KDCpGPJAB0Eb2HzAEECA92bwBoS6DsoQHgnXsBX068zMD/DeTH0z8C0LZKW8Y3+ZABy//F11WLeMwm2DULds0GFChd0zLkG563vRuFEAXDL9CxlapCeDoJAF3Eo+YAunk/YGtg5alDwNZE0KkGE98c+4YhG4dwM/Umvl6+zG4zm+FNhvPH+XjARQtZEq5Yev3O3R4OaNAH2s8GrX/e6xZCCCFcQAJAF/GUPIDg/mTQnr4IxNquY9dO8fTavgA0LNuQz7t/Ts3SNQEXzmM8tR3WvQgp18E7ADrPg7rP5K1OIYQQwsUkAHQRnYfkAYT0APC6mxeBeGoamBM3/gIgLiUZtZ+aCY9OYGLLiWg16fs2ZrcNnENMRtjxLuydazkuU8cy5Fuqal6aLoQQQuQLCQBdJH0v4IIPggqqB9BP61k/Tjqjjrd3vM2Cvf8jgrl4qwPYM2APD1d4OEvZPAWxty5Zcvtd2G85bjwQIqeDt19emi+EEELkG8/6xC7EdCYPWgV8OwBM1BlJ1ZvyfXGGJ/YA/nX1L3qv782Rq0fwxrJ3b4hPeLbBH2TeBs4h/26F9UMg9SZog6DLAqjdI09tF0IIIfJbwUcrRYQ1D6AnLAIJ8vGyBaLu6AX0pDmAZsXM3P1zabykMUeuHqG0f2miOlkSdqblsBewwyuZTQbY+jasftoS/JWtZ9nOTYI/IYQQhUDBRytFhCelgVGpVOmpYJLS8v16ntIDeOHWBdp82oYxW8egN+npdH8n/nrpL7rWbAdYAkCzOfuEoKmOrGSOvwAr2sO+25ngmwyBQdugZBWX3IcQQgiR32QI2EU8KRE0WFLB/Hcz1S09gCl6I1BweQAVRWH1X6sZvnk4t3S3CPAO4IPID3ih4QuoVCpb+8ASrAb4ZP2xt3sRyD+bYMMwSIsHnxLQ9UOo1cWVtyOEEELkOwkAXcSTVgGDexeCpN4OngqiB/Bm6k1e2vQSX//9NQAP3fMQn3X/jKph6atvfTP0yt4tAMy1F9Ooh20T4cDHluPyjeCp5RBayTU3IoQQQriRBIAuYs0DqNUUwwDwdg+bu+cAbju9jf7/68/lxMtoVBomtZzEhOYT8FJn/rFWq1X4eqtJM5iz3Q8YMswBzC4AvHkWvhkAlw9bjpuNgMcngZc2a1khhBCiEJAA0EXSewALfg4gQOlAX8A9u4HYkii7KQBMNaQy/qfxLPjNMgevesnqfNb9Mx4s/+Bdz/HXepFm0GfaDziju64C/nsDfPcy6BLANwS6R0H19q64DSGEEKLASADoAoqiZFgE4lk9gNcSitYq4D+u/EHvdb05fv04AMMaD+O9tu/h753zNmt+d+wHfKcsO4EY0mDrm/D7Ustxhabw5DIIqeCCuxBCCCEKlgSALmDt/QPPCwDd0gOoz/9VwCazidm/zGbizokYzUbKBpZledfltKvazq7zrQtU7jYEnGkV8I3TsLY/xByxvPnIq/DYW6DxzuttCCGEEB5BAkAX0JsyBoCeMQQc7qY5gIqikJLPaWDOxJ2h7/q+/PLfLwA8WfNJPun0CSX9S9pdh7V3MtVgzPb9tNtBfLVrP8LPU0CfBP4lofsnUO2JPN6BEEII4VkkAHQBXYYEw94aVQG2JJ1tP+AkHWazglqdP+3SGc0ot1PruToNjKIorIhewcgtI0nSJxGkDeLDDh/Sp24fVCrH7sc3lyFgky6F6V5LafrHz5YX7n0YnloGweXydA9CCCGEJ5IA0AWsK4B9vNQOByb5pWSgZYWqwaRwK9VAaED+rFhNy7CowpU9gLHJsby48UU2/LMBgOb3NufT7p9SKaSSU/X55zQEHPsv714fSSWvcyioULV4DVqOB438egghhCia5BPOBTwtCTRYhqJD/L2JTzEQm6TLtwDQ2qOm1ajxclEKnE3/bmLgdwO5lnwNb7U37zz2DmOajUGjdj7AtAanWVYB/7kGNo6mkimZWCWYa20W8kDzbnlovRBCCOH5PCdiKcT0HpYCxqp0YP7PA0y9W/oUJyTrkxm6cSidvuzEteRrPFD6AX4f/DuvP/J6noI/yGYRiD4ZNgyH9UPAkMxBdR066Gagr9gyr7chhBBCeDzpAXQBT+wBBMs8wJPXkvI3ALSlgMnbj9KBiwfovb43p26eAmD0Q6N59/F38fXyzXMbIX0IOEVvgmvHLat8Y/8BlRpajmfYnvrEYrRvL2AhhBCikJMA0AV0t3vBtB4YAAJcS0zLt2vYtlBzcgGIUTEydfdUZvwyA5Ni4p7ge1jVbRWPVX7Mlc28PQSsUO3yBjgwF4ypEBgBTy6Fys1J2fFjhnJCCCFE0SYBoAuk9wB6VvDgjlQwKXnIAfjvjX+ZcHICJ1NOAtCzdk8WdVhEqF+oS9sIEKzR84H3R3Q6a0klQ5XHoPtiCCwN5D2QFUIIIQoTCQBdwJOHgCGf5wDqHQ+cFEUh6mAUY7aOIdWYSohvCB93/Jjnaj+XP42M+Ys+f/YlRHMeExo0j78Jj4wCteV5GUxmTGZLLhtfDwvihRBCiPwgAaALeNo2cFbu2A3EmljZ3m3griReYdB3g/jh1A8A1A2sy4b+G6hcsrLrG6cocGgF/DCeEJOOK0oYX1aYzOjm/TIVy7gy2FfrWc9QCCGEyA8SALqANQ+gx80BDLQsoMjfHkBL8GvP4ol1x9fx4vcvciP1Bj4aH6a3nk7l2MrcE3yP6xuWlgDfj4S/1wFwuXRzOv7Xi/qaKlmL3g4AVSpLOhshhBCiqJNPOxfw1DmA7hgCTtHn3gOYoEtgwP8G8OTXT3Ij9Qb1I+rzx5A/eLnJy6hV+fAjeDkaPmlhCf7UXvDENA49EkUcwVnzAAJpt4NYP2+NxyTyFkIIIfKT9AC6gHUVsI8LcuG5kjUAjEsxoDea86WHMi2XfYD3nN9D3w19ORd/DhUqxj86nsmtJqPVaDEYDK5tjKLAb0tg65tg0kOJCvDUcqjQBP/jV4HsdwJJzee9jIUQQghPIwGgC+hNnjkHMMTPGy+1CqNZ4XqSjnIhfi6/hnUV8J1DwHqTnkk7JjHrl1koKFQKqcRn3T/j0XsfdXkbAEiNh+9GwPHvLcfVO0LXD8E/DEgP7rLbCzjNkP09CCGEEEWVBIAuoDN45hCwWq2idJAPV26lEZuYPwGgtfcs4xDw39f+pte6Xvx59U8ABtYfyAftPiDYJ9jl1wfg4iH4pj/EXwC1N7SdBk2HWib13WbbCSSbIWBX7mYihBBCFAYSALqAp6aBATIFgPkh4xCwWTGz4MACxv80Hp1JRyn/UizutJjuNbvny7VRFPj1I9g2CcwGCKkIT6+A8o2yFM2yFVwGkgNQCCFEcSMBoAtYVwF7ZAAYmL+pYKxDqnolibaftWX72e0AdKjWgWVdlhERGJEv1yXlJmwYBv9a0slQswt0WQh+IdkW9/e2/Khn1wNoncMpOQCFEEIUFxIAuoCn9wBC/q0Etvaozd73DteU7fh7+zOn7RyGNBqSfytqLxyAbwZCwkXQaCFyOjz4QqYh3ztlHAJWFCVT26QHUAghRHEjAaAL2BJBe+AigvwMAONS4zhw8TBQjmTDTZpUbMJn3T/j/pL3u/xaAJjNsG8BbJ8KignC7oOnV0LZermeag3uFAXSDOZMwZ4juQyFEEKIosDzuqwKIWsPoCcmEc6vAHD7me3U+bgOV5PiAHj6gW7sHbA3/4K/5Ouw+hn4aZIl+Kv9JLy4y67gDzKneLlzGFhWAQshhChupAfQBWxzAD1wFamr5wCmGdOY8NME5h2YB0AlTQkUM/Rv+DzeGm+XXCOL8/ssQ76JV8DLF9rPgob9chzyvZNGrULrpUZvNJOiNxIWoLW9l54H0POenxDC9cxmM/Hx8XaVDQgIyN/GCFFAJAB0gfQ0MJ4XQFh7AK8lpuW5ruiYaHqt68Wx2GMADG00lOPHq3HuRmr+JFE2m2HvHNgxHRQzlKxmGfKNqO1Udf5aDXqj2dbjZ6WTHkAhipX4+HjmbjyMb0BQjuXSkhN5OdK5vzdCeDoJAF3AU7eCAwgPSt8P+M7FD/YymU28v+993t7xNgazgTIBZVjWZRkd7+/IQ0csq35z2grOKUnXYN2LcGaH5bjuc9BxDvgEOl2lv7eGeAxZkkHLTiBCFD++AUEEBIcUdDOEKDASALqA3oNXAZcKsgx1phnMJOmMBPk6Nkx7Lv4cfdf3Zc+FPQB0q9GNxZ0WUzqgNJAxibILg6czu2DdYEi6Cl5+0PF9qN/LoSHf7Phqs98NJF/uQQghhPBgEgC6gHUOYH7stZtX/lovAn28SNIZiU3U2R0AKorCqj9X8coPr5CoTyRQG8iCdgvoX79/5hQq+qw7gTjNbIJds2HXLECB0jUtQ77hNfJeN+ltzLoIRFYBCyGEKF48ImJZtGgRlSpVwtfXl6ZNm/Lbb7/dteySJUto3rw5oaGhhIaG0qZNmxzLu4MnDwGD4yuBr6dc56m1TzHgfwNI1CfySIVHODL0CAMaDMgU/BlNZts+yHkePk2MgU+7wq6ZgAINesPgn10W/EF6G+/cDUQWgQghhChuCvwT76uvvmL06NFMmjSJP/74g3r16hEZGcm1a9eyLb9z50569uzJjh072L9/PxUqVKBt27ZcunTJzS1PZwsAPTSAcGQl8A8nf6DOx3VYd3wd3mpvZjw+g139d1E5tHKWsmm37xvylkRZdWYHfPwInNsD3gHQfTF0XQRaf6frzI6f9vZuIHcEgGl6SQQthBCieCnwiGXu3LkMHjyYAQMGUKtWLaKiovD392f58uXZlv/iiy8YNmwY9evXp0aNGixduhSz2cz27dvd3PJ0njwHEOzrAUwxpDB803A6rO5ATFIMNUvV5MALBxj/6Hg06uwDoxS9EbBMzXPq3s1Gal5ei+bLZyDlOpSpDUN2Qb1nHa/LDtYevpQ7h4CNMgdQCCFE8VKgcwD1ej2HDh1iwoQJttfUajVt2rRh//79dtWRkpKCwWAgLCws2/d1Oh06XXrgk5iYCIDBYMBgMOSh9emsaUU0mF1WpyuFBVjm/cXEp2bbvoOXD9Lvu36cvHkSgJcffJl3Wr2Dn7dfjveTmGL5vvp5azAajY41KuEy6vWDuf/qAQBMDfphfuId8PaDfPoe+t4OUpPT9JnuK0Vnabu3Co98fu5ivffi/D3wFPIs8pfBYEBRTJjNWfcGz0hRTLZnYDIpdpe3fjl6DUfK23sNT2yTs9cwGvNpe9FiqkADwOvXr2MymShTpkym18uUKcM///xjVx3jxo2jXLlytGnTJtv3Z8yYwZQpU7K8vnv3bo4dO+Z4o7ORnKYBVOzbu4dTfi6p0qXiLqsADYf/Oc1m40nb6ybFxDdXv+GrmK8wY6akd0leufcV6hnqsWPbjlzrvZwM4IVaMbJ582a72xN+608anv8Eb1MSBrUv0fcO5DIPgR3XzIvYGDWg5s+jx9l8K/3ZX7theX5Hog9hOKfkaxsKg23bthV0E8Rt8izyR2JiIqcvqe3KA7gz7TQA586dwzfgRq7lt6WdJigoyKFrbLt9DUfK23sNT2yTs9fYf/l6jmWEYwr1KuCZM2eyZs0adu7cia+vb7ZlJkyYwOjRo23Hly5dolatWrRo0YJKlSq5pB2v/bYNUIhs8xhlS2TfjoKU8sclNl74G9+Q0nTo0AiAkzdPMuC7AfwWY1lA83TNp1nYbiFhftn3pGYn+r94OPIbIQF+dOjQIvcTTAbUO99Fc+ZDAMxl6rCrZF8e7tSb+t75tItIBtE/nGDf1fPcU6kKHSLTt6yb9+9eSEmhxcMP8WCl0Hxvh6cyGAxs27aNJ554Am83PA9xd/Is8tfNmzc5u+cM/kEhOZZLSYyn1UMV2LlzJ5UqVSIoJOe/DymJ8TzR/D7CwsIcusYTze8DcKi8vdfwxDY5e41mFavlWEY4pkADwFKlSqHRaLh69Wqm169evUpERESO577//vvMnDmTn376ibp16961nI+PDz4+PrbjhIQEALy9vV3yh9VsVjCYLL1GAb5aj/xjHRFiWUxxPcmAl5cXS/5YwqgfR5FiSKGETwk+6vgRz9d53uF6DWZLd7y/j1fu9x3/n2U7t4u3V2w3eRFT60kkb93usmeRm8DbKXD0JiXT9ayLWYL8fDzy+bmbu56HyJ08i/zh7e2NSqVBfZf5zVYqlcb2/ddoVHaXt345eg1Hytt7DU9sk7PX8PIq1H1WHqdAVy1otVoaNWqUaQGHdUFHs2bN7nre7NmzmTZtGlu2bKFx48buaOpdWdOgAPh46CIC6yrgq4mpdFnThSEbh5BiSKF1pdb89dJfTgV/4MAOGv9shqhHLcGfTwl45lPo8B54+eR8notZF3ncmQg6zZYI2jMX8QghhBCuVuDh9OjRo+nXrx+NGzemSZMmzJs3j+TkZAYMGABA3759KV++PDNmzABg1qxZTJw4kdWrV1OpUiViYmIACAwMJDDQ+W3CnKXLkApFq/HMACL89irg60k6/jixGR8vH2Y8PoORD41ErXK+zSm5pU8x6uGnSfDrR5bjcg3h6RUQWsnpa+bF3RJBy04gQgghipsCDwCfffZZYmNjmThxIjExMdSvX58tW7bYFoZcuHABtTo9SPn444/R6/U89dRTmeqZNGkSkydPdmfTgfRdQFQq8NZ43gqlRF0i43eMRqELKtTULv0QXz79CbXD877BeY49gHHnYO0AuPyH5fih4dBmMnhp83xdZ9kCwAw9gIqi2HYCkTyAQgghiosCDwABRowYwYgRI7J9b+fOnZmOz507l/8NcoDOkJ4DUJXHvWpdbd9/++izvg9n4s5wDy3REMrnXf5H7fBSLqk/fRu4O36Mjv0P/vcy6G6Bbwh0+xhqdHDJNfMiuyHgjD240gMohBCiuPDMMctCxDoH0JO2gdOb9Ly5/U2ar2jOmbgz3FviXiqXLAlAXIo5l7Ptl2Xo1JAGm16Dr/tagr97msDQvR4R/EF6oJpxCDhjb6CvhybyFkII4bjJkyejUqkyfdWokb69aFpaGsOHD6dkyZIEBgby5JNPZlmUeuHCBTp27Ii/vz/h4eGMHTs2S97bnTt30rBhQ3x8fKhatSorV650x+3lmXzi5VHGHkBPcDz2OM2WNWP63umYFTP96vXjyNAjVLodANq7H7A9Umw9gBq4cRqWPQG/L7G8+chIGLAZQiq47Hp5ld1ewNZgUKtR4+WhcziFEEI454EHHuDKlSu2r71799reGzVqFN9//z1r165l165dXL58mR49etjeN5lMdOzYEb1ez759+1i1ahUrV65k4sSJtjJnz56lY8eOtG7dmujoaF599VVeeOEFfvzxR7fepzM8Ygi4MLPOAdQWcABoVsx8+NuHjPtpHGnGNML8wljcaTFP1noScGw/YHtZV882SvwZPpkN+kTwC4Pun8D9bV12HVfxy2YRiPUePHUfZyGEEM7z8vLKNq3crVu3WLZsGatXr+axxx4DYMWKFdSsWZNff/2Vhx56iK1bt3Ls2DF++uknypQpQ/369Zk2bRrjxo1j8uTJaLVaoqKiqFy5MnPmzAGgZs2a7N27lw8++IDIyEi33quj5FMvj3QesA/wpYRLtPu8HSO3jCTNmEa7qu04+tJRW/AH9u0H7ChDWgrTvZbS7fTbluDv3octQ74eGPxBeg9gSjY9gLmmshFCCFHonDx5knLlynHffffRq1cvLly4AMChQ4cwGAyZdhGrUaMG9957r20r2v3791OnTp1Mu5VFRkaSkJDA33//bStz505kkZGRdm9nW5CkBzCP0gPAggkgvv77a4ZuHEpcWhx+Xn6898R7DHtwWJYFKS4PAK+fZPCJwZTzOo2CClXzMdBqAmg890cqfRVw+vyNNEkBI4QQhUpiYqJtUwfIuuGDVdOmTVm5ciXVq1fnypUrTJkyhebNm3P06FFiYmLQarWEhIRkOqdMmTK29HIxMTHZblVrfS+nMgkJCaSmpuLn54H7w97muZ/WhYTeGgC6eQgxPi2el394mc+PfA5A43KN+az7Z9QoVSPb8i4NAP/8CjaOopwhmVglmMONZ9P28Z55rzefZcwDqCgKKpUqPQWMBIBCCFEo1KpVK9Px3dLAtW/f3vbvunXr0rRpUypWrMjXX3/t0YGZu0gAmEe2OYBuXECw89xO+q7vy38J/6FWqXnj0TeY2HIi3pq7bxnlkjmA+hTYPBaiLUHncZ/69L01mLFlH3W+TjfyvR0AmhVLz62vt8a2IMRXcgAKIUShcOzYMcqXL287zq73LzshISHcf//9nDp1iieeeAK9Xk98fHymXsCMW9FGRETw22+/ZarDuko4Y5nstrMNDg72+CBT5gDmkW0VsBt6kHRGHa9tfY3HVj3Gfwn/USW0CnsH7GXaY9NyDP4AwoNvB4AJTgaA147Dkta3gz8VtJrA1LB3iSW00PSeZWyndeg37XYALylghBCicAgKCiI4ONj2ZW8AmJSUxOnTpylbtiyNGjXC29s701a0J06c4MKFC7ataJs1a8Zff/3FtWvXbGW2bdtGcHCwrReyWbNmmeqwlslpO1tPIZ96eeSuRSBHrh7hwSUPMmf/HBQUBjccTPTQaJpVsO+HzDoEnKgzZkqDkitFgcOfw+LWEPsPBJaBft9Bq/GkGCxF/AtJ75m3Rm3brcW6ECQ1t+3shBBCFEqvvfYau3bt4ty5c+zbt4/u3buj0Wjo2bMnJUqUYNCgQYwePZodO3Zw6NAhBgwYQLNmzXjooYcAaNu2LbVq1aJPnz78+eef/Pjjj7z11lsMHz7cFnQOHTqUM2fO8Prrr/PPP//w0Ucf8fXXXzNq1KiCvHW7yBBwHulv9yDlVwBoMpuYu38ub+14C71JT3hAOEs7L6Vz9c4O1RPk44WPlxqd0cz1JB0VwvxzP0mXBJtGw5GvLMf3tYYeSyCwNFA4V9D6eWswmIy2tqcVwnsQQgiRu4sXL9KzZ09u3LhB6dKlefTRR/n1118pXdryGfbBBx+gVqt58skn0el0REZG8tFHH9nO12g0bNy4kZdeeolmzZoREBBAv379mDp1qq1M5cqV2bRpE6NGjWL+/Pncc889LF261ONTwIAEgHmWn6uAz8efp9+Gfuw6vwuALtW7sKTzEsIDwh2uS6VSUTrIh4txqVxLtCMAjDkKa/vDjZOgUkPrN+HR0ZBhX+aUQth75q/1IiEtvRfUughEVgELIUTRsmbNmhzf9/X1ZdGiRSxatOiuZSpWrMjmzZtzrKdVq1YcPnzYqTYWJAkA88gaALoyEbSiKHx+5HNG/DCCBF0CAd4BzGs3j0ENBuVpv2FrAJjjSmBFgUMr4YdxYNJBUDl4ahlUfDhLUVvvWSEKAK1ttQ0BSxoYIYQQxZAEgHmkc/EQ8I2UG7y06SXWHlsLQLN7mvFZ98+oElYlz3XnuhI4LQE2vgpHv7UcV33CsqtHQMlsi9u2gvMuPD9Gtu3gDJkDQBkCFkIIUZwUnk9uD5W+CjjvAeCPp35kwP8GcCXpCl5qLya3nMy4R8fhpXbNY8oxF+CVPy1DvjfPgEoDbSZBs5czDflmpChKeu+ZtvCsJfK7Ixl0eiLownMPQgghRF5JAJhHelPe5wCmGFIYt20cH/7+IQDVS1bn8x6f07hcY5e00SrbAFBR4Pel8OMbYNJDiQrw1HKo0CTHunRGM4pi+be/tvD8GPnfsR+wLAIRQghRHBWeT24PZesBdHII+NDlQ/Re35t/rv8DwMtNXmZmm5n4e9uxStdB4UG+AMQmplleSI2H716G499Zjqt3gK6LwD8s17oyppIpTDn07twP2JYIWgJAIYQQxYgEgHnk7BxAo9nIzL0zmbJrCkazkbKBZVnRdQWRVfNv6XimHsBLh2DtAIg/D2pveGIqPPQS2LnIxNqDptWo8XLjLih5lT4EfMcq4EK0kEUIIYTIKwkA88iZRNCnb56mz/o+7L+4H4Cnaz3Nxx0/pqR/9ostXMUSACq0jvsGln0OZgOEVISnV0D5Rg7VVRhTwECGIWC9LAIRQghRfEkAmEeO5AFUFIWlfyxl1I+jSDYkE+wTzIftP6R33d55Su9ir3DvFJZ4z+UJ0yHLCzW7QJeF4BficF2Fde6cdag35Y45gLIIRAghRHEiAWAe6Y32rQK+lnyNwd8P5rsTlvl2LSu2ZFW3VVQMqZjvbQTgv98ou3YA5TQX0SlemJ54B/9Hhto95HsnWwqYQt4DWFgDWSGEECIvpNsjj6xzALU5zIP7/sT31P6oNt+d+A6tRst7T7zH9r7b3RP8mc3wy3xY0R5VwkUuUIYe+ilcqtbb6eAPCm8CZVseQBkCFkIIUYxJD2Ae6XLoAUzSJzH6x9Es+WMJALXDa/NFjy+oW6auexqXfAM2DIWTWy3HD/RgxIVn+DvNTGyijmplgpyu2ppHr7D1APrdTlmTngbG+vwK130IIYQQeSEBYB6lp4HJHEDs/28/fdb34XTcaVSoGN1sNO889g6+Xr7uadj5ffDNIEi8DBofaD8LGvUncOkBiL3BtZy2g7NDaiHcBg7SA9Y7t4KTHkAhhBDFiQSAeZSeCNrSA2gwGZi6ayrT907HrJipEFyBVd1W0bpya/c0yGyGvXNhx3RQTFCyGjy9EiJqA7nsBuIA2yrgQhY4pW8Fd3snkEK6mlkIIYTICwkA80hnsOYB1HDi+gl6r+/NwcsHAehdtzcL2y8kxDfEPY1JioV1g+HMDstx3Weh41zwCbQVyXU/YDulFtLAKUseQKOsAhZCCFH8SACYR9Y5gN8e/4rZv71KqjGVUN9QojpF8cwDz7ivIWd3w7cvQNJV8PKDju9D/V5ZFnq4qgcwtZCuAs64E4jRZMZgUjK9LoQQQhQHEgDmkXUo8d29kzGqU3nividY0XUF5YPLu6cBZhPsfg92zQLFDKVrWIZ8w2tmW9xlAWAhXQVsDVjTDCbSbgfvUPjuQwiRzmw2Ex8fn2u5kJAQ1Grp7RcCJADMk2+PfUuy3gsVXmi9VMxtu4DhTYajVrnpD0xijGXI9+xuy3H93tBhNmgD7nqKqwPAQtcDmGERSMb9jJ3dy1kIUfDi4+OZu/EwvgF3z2yQlpzI6E4NCAvLfa9zIYoDCQCdcCvtFq9seYVPoz+jIt8D8FPfLTSrWMd9jTj9M6x7EZJjwTsAOs2Fes/lepotAHTVHMBC1nOWMQ9gxiTQ7tiJRQiRf3wDgggIDinoZghRaEgA6KDd53fTd31fzt86j0aVntKlTkT2Q64uZzLCzhmwZw6gQPgDliHf0vfbdXp4kKXNN5P1GExmvHNIYJ2TwjsEnJ4HULaBE0IIUVzJJ5+ddEYd47aNo9XKVpy/dZ77Qu9jc8+ttvfdMoR46xKs6gx73gcUaDQABm+3O/gDCPHzxktt6e26nodewPSt4ArX/yGsPYBGs0JCmiHTa0IIIURxUbg+vQvI0WtH6b2uN39e/ROAQQ0G8UHkB6TqvIHtqFXYgqp8c3KbZcg39SZog6DzPKjzlMPVqNUqSgX6EJOQRmyijrIl/Jxqjm34VFu4/g+RMW3NzWRLAOhbyOYxCiGEEHklAWAOzIqZeb/OY8L2CehNekr5l2JJ5yV0q9ENgPjkFMCSAzDf5pCZDPDzNMt+vgARdS1DviWrOF1l6aD0ANBZ6YmgC9ePkLdGhUatwmRWiEvWA+DrJQGgEEKI4qVwfXq70X+3/qPfhn7sOGdJqtyxWkeWdVlGmcAytjI57QPsEvH/wTcD4eJvluMHB0Pbd8A7b9vJuWIlcGFNBK1SqfD31pCoM3LjdgBY2O5BCCGEyCsJALOx+q/VDNs0jFu6W/h7+/NB5AcMbjg4Sy+f7vYuElonF1Lk6J/NsOElSIsHnxLQdSHU6uqSqm27geQlACykaWDAEvAl6ozEpdzuAXTjIhCTyYTBYHDb9RxhMBjw8vIiLS0Nk8mU+wki33j6s9BoNHh5ecnqeSEKMQkAM4hLjWPY5mGsOboGgKblm/JZ98+oVrJatuXzpQfQqIefJsOviyzH5RrCU8shrLLLLuGKVDCFNQ0MpPf43bT2ALrpHpKSkrh48SKKorjleo5SFIWIiAj+++8/+WAvYIXhWfj7+1O2bFm0Wm1BN0UI4QQJAG/76cxP9N/Qn0uJl9CoNExqOYkJzSfgpb77t0hnuB0AumoOWdw5y5DvpUOW44eGQZsp4OXaP7DhwXnvAUzRW3ZAKYzDp9aAzzYH0A0BoMlk4uLFi/j7+1O6dGmP/FA3m80kJSURGBgouyUUME9+FoqioNfriY2N5ezZs1SrVs3j2iiEyJ0EgLdFHYziUuIl7i95P593/5wHyz+Y6zl6kzUAdMEfv2Pfwf9GgO4W+IZAt4+hRoe815sN6xDwtTwEgGm3g99C3QOY4r4A0GAwoCgKpUuXxs/PuZXX+c1sNqPX6/H19ZUP9ALm6c/Cz88Pb29vzp8/b2unq9i7rRvI1m5C5IUEgLdFdYqiSmgVJracSEAOW6llpLs9Dy5PAaBRB1vfgt8WW47vedAy5Btyr/N15iKvi0CMJrMt+C2McwCtbY5z8xAw4JE9f0I4I78CL3u2dQPZ2k2IvJIA8LZS/qWY9cQsh86xzgHUOhsA3jgN3wyAK5b8gjwyEh57GzTeztVnp4wBoKIoDgcl1gUgUPh2AoH01DU3ZRWwEB5JtnUTIv9JAJgHtkUgzswBPPotfDcS9IngFwbdP4H727q4hdkrdXsIONVgIllvItDHsR8DawCoVrlpBxQXswZ8CWmWeYy+hfAehBBCiLyQADAP9EYn5gAaUmHLBDi0wnJ8bzN4chmUKJ8PLcxegI8XAVoNyXoTsYk6xwPADCuAC+OQpv8dvZYFtROII3OdXKWwzZk6d+4clStX5vDhw9SvX7+gmwPAP//8Q//+/YmOjqZGjRpER0e77dqTJ09mw4YNbr2mEKJokgAwD6x5AH3sHQa9fhLW9oerRwEVNB8Nrd4AjfsfQ+kgH5JvpBCbqKNyKfvmPFqlGgpnEmirO9tdUAtZ7J3r5CrOzJnq378/q1atYsaMGYwfP972+oYNG+jevbvHprTJT5MmTSIgIIATJ04QGBjo1mu/9tprvPzyy269phCiaJIAMA9scwDtSQT951ewcRQYksG/FPRYDFUfz+cW3l14kC/nbgeAjkoppLuAWN3Z7oKcx1gY5jr5+voya9YshgwZQmhoaEE3xyX0er3T+etOnz5Nx44dqVixoluul1FgYKDbg04hRNFUeMaCPJAtD2BOiaD1KfC/4bD+RUvwV6k5vPRLgQZ/kHEhSJrD56YV4iTQkHUIuLDeh7u0adOGiIgIZsyYcdcykydPzjJEO2/ePCpVqmQ77t+/P926dWP69OmUKVOGkJAQpk6ditFoZOzYsYSFhXHPPfewYsWKLPX/888/PPzww/j6+lK7dm127dqV6f2jR4/Svn17AgMDKVOmDH369OH69eu291u1asWIESN49dVXKVWqFJGRkdneh9lsZurUqdxzzz34+PhQv359tmzZYntfpVJx6NAhpk6dikqlYvLkydnWc7fr5dTOxYsXU65cOcxmc6a6unbtysCBA+/6fV66dCk1a9bE19eXGjVq8NFHH9nee+qppxgxYoTt+NVXX0WlUvHPP/8AlsA0ICCAn376CYBvvvmGOnXq4OfnR8mSJWnTpg3JycnZ3qMQonCTADAPbEPAd5sDeO0fWPIYHP4cUEHL8dD3fxAU4b5G3oU1AHQmF2B6D2Dh7EDO2gMovwY50Wg0TJ8+nYULF3Lx4sU81fXzzz9z+fJldu/ezdy5c5k0aRKdOnUiNDSUAwcOMHToUIYMGZLlOmPHjmXMmDEcPnyYZs2a0blzZ27cuAFYhtIfe+wxGjRowMGDB9myZQtXr17lmWeeyVTHqlWr0Gq1/PLLL0RFRWXbvvnz5zNnzhzef/99jhw5QmRkJF26dOHkyZMAXLlyhQceeIAxY8Zw5coVXnvttbve653Xy62dTz/9NDdu3GDHjh22Om7evMmWLVvo1atXttf44osvmDhxIu+++y7Hjx9n+vTpvP3226xatQqAli1bsnPnTlv5Xbt2UapUKdtrv//+OwaDgYcffpgrV67Qs2dPBg4cyPHjx9m5cyc9evQolsP8QhQH8smXB/q7rQJWFEvQt7gVxB6HwDKWwK/1BFB7Rm9TXnIB2uYAFtLAyZOGgAuL7t27U79+fSZNmpSnesLCwliwYAHVq1dn4MCBVK9enZSUFN544w2qVavGhAkT0Gq17N27N9N5I0aM4Mknn6RmzZp8/PHHlChRgmXLlgHw4Ycf0qBBA6ZPn06NGjVo0KABy5cvZ8eOHfz777+2OqpVq8bs2bOpXr061atXz7Z977//PuPGjeO5556jevXqzJo1i/r16zNv3jwAIiIi8PLyIjAwkIiIiByHY++8Xm7tDA0NpX379qxevdpWxzfffEOpUqVo3bp1tteYNGkSc+bMoUePHlSuXJkePXowatQoPvnkE8DSE3ns2DFiY2OJi4vj2LFjjBw50hYA7ty5kwcffBB/f3+uXLmC0WikR48eVKpUiTp16jBs2DAZchaiiCqcn+AeQpfdKmBdEqwfahn2NabCfa1g6F64r2XBNPIurLuBOLMfsHUVsH9h7QGUIWCnzJo1i1WrVnH8+HGn63jggQcyrUIuU6YMderUsR1rNBpKlizJtWvXMp3XrFkz27+9vLxo3LixrR1//vknO3bssM2PCwwMpEaNGoBlvp5Vo0aNcmxbQkICly9f5pFHHsn0+iOPPOLUPd95PXva2atXL7799lt0Osvv5Zdffslzzz2X7crt5ORkTp8+zaBBgzLV+c4779jqq127NmFhYezatYs9e/bQoEEDOnXqZBtC37VrF61atQKgXr16PP7449SpU4enn36aJUuWEBcX5/B9CyEKh8L5Ce4hrEPAtkTQMUctiZ2v/wsqNbR+Ax4dAx6YdsM1PYCFM3C6c/cS6QG0T4sWLYiMjGTChAn0798/03tqtTrLUKHBYMhSh7d35iTnKpUq29funAeXk6SkJDp37sysWVkTuZctW9b274AAx1a759Wd17OnnZ07d0ZRFDZt2kSNGjXYs2cPH3zwQbb1JyUlAbBkyRKaNm2a6T2NxvIzrVKpaNGiBTt37sTHx4dWrVpRt25ddDodR48eZd++fbZhbI1Gw7Zt29i3bx9bt25l4cKFvPnmmxw4cIDKlSs7/X1wdGs3IYR7SACYB7YeQI0KDq6ALePBmAZBZS25/So9kksNBScvAWBhXwV8Z8BXWO+jIMycOZP69etnGUItXbo0MTExmXaWcWWuul9//ZUWLVoAYDQaOXTokG1xQ8OGDfn222+pVKkSXl7O/0kLDg6mXLly/PLLL7Rsmd5j/8svv9CkSZO83YCd7fT19aVHjx6sXr2aevXqUb16dRo2bJht2TJlylCuXDnOnDlz1zmCYJkHuGTJEnx8fHj33XdRq9W0aNGC9957D51Ol6nHU6VS8cgjj/DII48wceJEKlasyPr16xk9erTT9+3o1m5CCPeQADAP9EYzgaTw+LE3IOb2SsGqT0D3KAgoVbCNy4U1ALyRrMdkVtCo7U/oXPh7ADP/2BfkfaQlJxaqa9WpU4devXqxYMGCTK+3atWK2NhYZs+ezVNPPcWWLVv44YcfCA4OzvM1ARYtWkS1atWoWbMmH3zwAXFxcbaVscOHD2fJkiX07NmT119/nbCwME6dOsWaNWtYunSprTfMHmPHjmXSpElUqVKF+vXrs2LFCqKjo/niiy/yfA/2trNXr1506tSJo0eP0qdPnxzrnDJlCq+88golSpSgXbt26HQ6Dh48SFxcnC1oa9WqFaNGjUKr1fLoo4/aXnvttdd48MEHbT2VBw4cYPv27bRt25bw8HAOHDhAbGwsNWvWzPO9F4Z0R0IUNxIA5kGZ5BN8r51M5ZiroNLA4xPh4Vc8csj3TiUDtKhUYDIrxKXobdvD2SPNYJ0DWFgDwMztzjGNTz4KCQlxe4+HK4bYpk6dyldffZXptZo1a/LRRx8xffp0pk2bxpNPPslrr73G4sWL83w9sPQ8zpw5k+joaKpWrcp3331HqVKW/2RZe+3GjRtH27Zt0el0VKxYkXbt2jm868krr7zCrVu3GDNmDNeuXaNWrVp89913VKtWLc/3YG87H3vsMcLCwjh58iQ9e/bMsc4XXngBf39/3nvvPcaOHUtAQAB16tTh1VdftZWpU6cOISEh3H///bYFHa1atcJkMtnm/4GlB3T37t3MmzePhIQEKlasyJw5c2jfvn2e710I4XkkAHSGosDvS3nzygS81QZS/CLw7/kp3Ns093M9hJdGTckALdeT9FxL0DkUAKbob++hW0h7ALMMARfQfajVaod25SgIK1euzPJapUqVbIsUMho6dChDhw7N9Nobb7yRY10ZU5RYnTt3LtO1rHMLcwqGqlWrxrp16+76fnbXyY5arWbSpEk5rna2Z2j7btfLrZ3WNly8eJGEhIQsPaiTJ0/Oknvw+eef5/nnn8+xvps3b2Z6rX79+lnmbNasWTNTzkMhRNEmAaCj0m7Bdy/Dsf/hDWwzNUT9xEc8fm/eh0ncrVSgD9eT9A6vBE7VW+Y+Fta5c7IIRAghRHHn+WOVnuTSIYhqDsf+B2pvlvq/wGDDGNT+JQu6ZU5xdiFIqsHSA1hYh4Az9vh5qVV427OVnxBCCFGESA+gPRQFDkTB1rfBbICQe+Gplaxdmwwk3n0nEA/ndAB4exVwYe05y9hzWVgXsghREBRFwWSy/P4bjUZbipfs9jkOCQlxeA6mEMJ9JADMTcpN+N8IOLHJclyzM3T5EPxC0BktWzYV1CKCvHI2AEzRF+5FID5eatQqMCvgIwGgEHYzmUxcjU9BpdFg1OtJTDOw9bcLJN+R8tGa0sXT57gKUZxJAJiT/363JHa+9R9otND2XWgyGG7nObPmAdQ6kGbCkzi7G0haIU8Do1Kp8PPWkKw34ad1b/Au+6oKT5KxRy83tuTSGg0ajQazWo1KpcYvIBjMhfM/wUIUZxIAZsdshv0LYftUMBshtDI8vRLK1c9UzJYIupD2AIYH+wIQm5jm0HmFPRE0gJ/WyxIAuimItX546vV6/Pz83HJNIXKTsUcvJ4rJRJkQ/0yv6XVpmBQFvdn+HKJCCM8hAeCdkm/Ahpfg5I+W4wd6QOf54Js1oa3udk9YoZ0DGOjsIpDC3QMI2Hr+3DWP0cvLC39/f2JjY/H29vbIuVFmsxm9Xk9aWppHtq84cfZZ2Nujp9FoUKlUGI1GTCYjanLumTabTKSlWf6jaNDrSDMYuHE9lsspakxIAChEYSQBYEbn98M3AyHxMmh8oP1MaDTANuR7J73pdg+gV+EMhKxzAK85uQjkzh01ChN/b0vb3RUAqlQqypYty9mzZzl//rxbrukoRVFITU3Fz8/Ptp2bKBjOPguz2UxCih5VDjv7KGaFYH8tarUas9lMYpoBlSrnIFNRzCT6WvZsTkg1oKhUXE5R819a1sUfQojCofB+gruS2Qy/fAA/vwuKCUpWtQz5RtS56ykms4LBZPlfc6HtAbwdACamGUkzmOwOhopCD6Dv7eFrd96DVqulWrVq6PV6t13TEQaDgd27d9OiRQu8vb0LujnFmrPPIj4+nq2nL+CXw767qcmJ9GxSlpCQEEv533Iun35OBAA/HriAl38J6fkTopCTADApFta/CKd/thzXfRY6zgWfwBxP09+e/wegLaQBYLCvF1ovNXqjmdhEHRXC/HM9R1GU9ACwEM8B9L8d+Pm6ef6mWq3G19fXrde0l0ajwWg04uvrKwFgAdNoNOj1elJSUux6FtaUK1qt1rIi13z3381kg+U/I76+vnaVz3gOQIoRAiT4E6LQ84gAcNGiRbz33nvExMRQr149Fi5cSJMmTe5afu3atbz99tucO3eOatWqMWvWLDp06OD4hc/ugW9fgKQY8PKDDu9Bg953HfLNSGdMn2dTWHsAVSoVpQN9uBSfSmySfQGgzmjGupC1UAeABdADKIo3a848ewQEBJCcnMz8H/7EPygkx7KSckWInDkaYxQXBR4AfvXVV4wePZqoqCiaNm3KvHnziIyM5MSJE4SHh2cpv2/fPnr27MmMGTPo1KkTq1evplu3bvzxxx/Url3b7uuqDnwM/ywGxQylqsMzqyDc/u3crD2AGrUKr0K8k0TpoNsBoJ3zAK3z/6BwB0+2IeBCHMSKguNIMGftnYuPj2fuxsP45jLcmpacyMuRlr9lfoFBBASH5LG1QhRfjsYYxUmBB4Bz585l8ODBDBgwAICoqCg2bdrE8uXLGT9+fJby8+fPp127dowdOxaAadOmsW3bNj788EOioqLsvq7mwEcQrIb6vaHDbI5dN3Hh6BW7z7+eZJnHVVh7/6zCb88D3P1vrF056uJSLBlftV5qNDlMNPd01iHgwrqApzhxJtiy9xxHy1vPcSSYy9g75xsgAZ0Q7uRojFGcFGgAqNfrOXToEBMmTLC9plaradOmDfv378/2nP379zN69OhMr0VGRrJhw4Zsy+t0OnS69N6tW7duAXA5xYvrD41DqRgJf//LJ3vO8r8/Y7Kcr/EJsP3bpEvO8r6vr4bDhw9nei00NBSAuLi4bNt0J2t5Z87J6zXMN86ji73K8o3nWZ6hfG737eOT9/sODAzk+vXrnDt3jqSkJLvOcdV9J14+iy42hviLBg4f1uda3h1tKshrhIaGYjAYuH79OtHR0Xh55f6nwV33HRcXx8c/Hkbrm/MUBX1aCi9FNrD7HEfLZzwH4FbcjUx/W7KTlpLE+fPnSUhIIC4ujqsXz+Hrn/P84rSUJC5cUHHz5k2uGL0ICMr5e2W5hrfd13C0fMZzAIfKe9I1nG1Tfj6LjOeAZ923Jz7vSz6W389bt24RHJyems3HxwcfH58s5zgTYxQrSgG6dOmSAij79u3L9PrYsWOVJk2aZHuOt7e3snr16kyvLVq0SAkPD8+2/KRJkxRAvuRLvuRLvuRLvorg16RJk1wWYxQnBT4EnN8mTJiQqcfw5s2bVK5cmaNHj1KiRIkCbJlITEykVq1aHDt2jKCgnIfSRP6T5+E55Fl4DnkWnuPWrVvUrl2bs2fPZlr0lF3vn8hdgQaApUqVQqPRcPXq1UyvX716lYiIiGzPiYiIcKj83bqGK1SokKkLWbhfQkICAOXLl5dn4QHkeXgOeRaeQ56F57B+/8PCwux6Fs7EGMVJga5g0Gq1NGrUiO3bt9teM5vNbN++nWbNmmV7TrNmzTKVB9i2bdtdywshhBCi+HEmxihOCnwIePTo0fTr14/GjRvTpEkT5s2bR3Jysm3FTt++fSlfvjwzZswAYOTIkbRs2ZI5c+bQsWNH1qxZw8GDB1m8eHFB3oYQQgghPExuMUZxVuAB4LPPPktsbCwTJ04kJiaG+vXrs2XLFsqUKQPAhQsXMm2G/vDDD7N69Wreeust3njjDapVq8aGDRvszgHo4+PDpEmTZM6AB5Bn4VnkeXgOeRaeQ56F53DmWeQWYxRnKkWxI/mbEEIIIYQoMgp3FmMhhBBCCOEwCQCFEEIIIYoZCQCFEEIIIYoZCQCFEEIIIYqZIhkALlq0iEqVKuHr60vTpk357bffciy/du1aatSoga+vL3Xq1GHz5s1uamnR58izWLJkCc2bNyc0NJTQ0FDatGmT67MTjnH0d8NqzZo1qFQqunXrlr8NLEYcfRbx8fEMHz6csmXL4uPjw/333y9/q1zE0Wcxb948qlevjp+fHxUqVGDUqFGkpaW5qbVF1+7du+ncuTPlypVDpVKxYcOGXM/ZuXMnDRs2xMfHh6pVq7Jy5cp8b2eRUdB70bnamjVrFK1Wqyxfvlz5+++/lcGDByshISHK1atXsy3/yy+/KBqNRpk9e7Zy7Ngx5a233lK8vb2Vv/76y80tL3ocfRbPP/+8smjRIuXw4cPK8ePHlf79+yslSpRQLl686OaWF02OPg+rs2fPKuXLl1eaN2+udO3a1T2NLeIcfRY6nU5p3Lix0qFDB2Xv3r3K2bNnlZ07dyrR0dFubnnR4+iz+OKLLxQfHx/liy++UM6ePav8+OOPStmyZZVRo0a5ueVFz+bNm5U333xTWbdunQIo69evz7H8mTNnFH9/f2X06NHKsWPHlIULFyoajUbZsmWLexpcyBW5ALBJkybK8OHDbccmk0kpV66cMmPGjGzLP/PMM0rHjh0zvda0aVNlyJAh+drO4sDRZ3Eno9GoBAUFKatWrcqvJhYrzjwPo9GoPPzww8rSpUuVfv36SQDoIo4+i48//li57777FL1e764mFhuOPovhw4crjz32WKbXRo8erTzyyCP52s7ixp4A8PXXX1ceeOCBTK89++yzSmRkZD62rOgoUkPAer2eQ4cO0aZNG9trarWaNm3asH///mzP2b9/f6byAJGRkXctL+zjzLO4U0pKCgaDIdOm38I5zj6PqVOnEh4ezqBBg9zRzGLBmWfx3Xff0axZM4YPH06ZMmWoXbs206dPx2QyuavZRZIzz+Lhhx/m0KFDtmHiM2fOsHnzZjp06OCWNot08vmdNwW+E4grXb9+HZPJlCXDd5kyZfjnn3+yPScmJibb8jExMfnWzuLAmWdxp3HjxlGuXLksv+DCcc48j71797Js2TKio6Pd0MLiw5lncebMGX7++Wd69erF5s2bOXXqFMOGDcNgMDBp0iR3NLtIcuZZPP/881y/fp1HH30URVEwGo0MHTqUN954wx1NFhnc7fM7ISGB1NRU/Pz8CqhlhUOR6gEURcfMmTNZs2YN69evx9fXt6CbU+wkJibSp08flixZQqlSpQq6OcWe2WwmPDycxYsX06hRI5599lnefPNNoqKiCrppxc7OnTuZPn06H330EX/88Qfr1q1j06ZNTJs2raCbJoRDilQPYKlSpdBoNFy9ejXT61evXiUiIiLbcyIiIhwqL+zjzLOwev/995k5cyY//fQTdevWzc9mFhuOPo/Tp09z7tw5OnfubHvNbDYD4OXlxYkTJ6hSpUr+NrqIcuZ3o2zZsnh7e6PRaGyv1axZk5iYGPR6PVqtNl/bXFQ58yzefvtt+vTpwwsvvABAnTp1SE5O5sUXX+TNN9/MtHe9yF93+/wODg6W3j87FKmfVK1WS6NGjdi+fbvtNbPZzPbt22nWrFm25zRr1ixTeYBt27bdtbywjzPPAmD27NlMmzaNLVu20LhxY3c0tVhw9HnUqFGDv/76i+joaNtXly5daN26NdHR0VSoUMGdzS9SnPndeOSRRzh16pQtCAf4999/KVu2rAR/eeDMs0hJSckS5FkDc0VR8q+xIgv5/M6jgl6F4mpr1qxRfHx8lJUrVyrHjh1TXnzxRSUkJESJiYlRFEVR+vTpo4wfP95W/pdfflG8vLyU999/Xzl+/LgyadIkSQPjIo4+i5kzZyparVb55ptvlCtXrti+EhMTC+oWihRHn8edZBWw6zj6LC5cuKAEBQUpI0aMUE6cOKFs3LhRCQ8PV955552CuoUiw9FnMWnSJCUoKEj58ssvlTNnzihbt25VqlSpojzzzDMFdQtFRmJionL48GHl8OHDCqDMnTtXOXz4sHL+/HlFURRl/PjxSp8+fWzlrWlgxo4dqxw/flxZtGiRpIFxQJELABVFURYuXKjce++9ilarVZo0aaL8+uuvtvdatmyp9OvXL1P5r7/+Wrn//vsVrVarPPDAA8qmTZvc3OKiy5FnUbFiRQXI8jVp0iT3N7yIcvR3IyMJAF3L0Wexb98+pWnTpoqPj49y3333Ke+++65iNBrd3OqiyZFnYTAYlMmTJytVqlRRfH19lQoVKijDhg1T4uLi3N/wImbHjh3ZfgZYv//9+vVTWrZsmeWc+vXrK1qtVrnvvvuUFStWuL3dhZVKUaTPWgghhBCiOClScwCFEEIIIUTuJAAUQgghhChmJAAUQgghhChmJAAUQgghhChmJAAUQgghhChmJAAUQgghhChmJAAUQgghhChmJAAUQni8/v37061bN9txq1atePXVV93ejp07d6JSqYiPj3f7tYUQwpUkABRCOKV///6oVCpUKhVarZaqVasydepUjEZjvl973bp1TJs2za6y7g7aKlWqZPu++Pv7U6dOHZYuXeqWawshhL0kABRCOK1du3ZcuXKFkydPMmbMGCZPnsx7772XbVm9Xu+y64aFhREUFOSy+lxt6tSpXLlyhaNHj9K7d28GDx7MDz/8UNDNEkIIlrZjpwAABZ9JREFUGwkAhRBO8/HxISIigooVK/LSSy/Rpk0bvvvuOyB92Pbdd9+lXLlyVK9eHYD//vuPZ555hpCQEMLCwujatSvnzp2z1WkymRg9ejQhISGULFmS119/nTt3rLxzCFin0zFu3DgqVKiAj48PVatWZdmyZZw7d47WrVsDEBoaikqlon///gCYzWZmzJhB5cqV8fPzo169enzzzTeZrrN582buv/9+/Pz8aN26daZ25iQoKIiIiAjuu+8+xo0bR1hYGNu2bXPgOyuEEPlLAkAhhMv4+fll6unbvn07J06cYNu2bWzcuBGDwUBkZCRBQUHs2bOHX375hcDAQNq1a2c7b86cOaxcuZLly5ezd+9ebt68yfr163O8bt++ffnyyy9ZsGABx48f55NPPiEwMJAKFSrw7bffAnDixAmuXLnC/PnzAZgxYwaffvopUVFR/P3334waNYrevXuza9cuwBKo9ujRg86dOxMdHc0LL7zA+PHjHfp+mM1mvv32W+Li4tBqtQ6dK4QQ+UoRQggn9OvXT+natauiKIpiNpuVbdu2KT4+Psprr71me79MmTKKTqeznfPZZ58p1atXV8xms+01nU6n+Pn5KT/++KOiKIpStmxZZfbs2bb3DQaDcs8999iupSiK0rJlS2XkyJGKoijKiRMnFEDZtm1btu3csWOHAihxcXG219LS0hR/f39l3759mcoOGjRI6dmzp6IoijJhwgSlVq1amd4fN25clrruVLFiRUWr1SoBAQGKl5eXAihhYWHKyZMn73qOEEK4m1fBhp9CiMJs48aNBAYGYjAYMJvNPP/880yePNn2fp06dTL1fP3555+cOnUqy/y9tLQ0Tp8+za1bt7hy5QpNmza1vefl5UXjxo2zDANbRUdHo9FoaNmypd3tPnXqFCkpKTzxxBOZXtfr9TRo0ACA48ePZ2oHQLNmzeyqf+zYsfTv358rV64wduxYhg0bRtWqVe1unxBC5DcJAIUQTmvdujUff/wxWq2WcuXK4eWV+U9KQEBApuOkpCQaNWrEF198kaWu0qVLO9UGPz8/h89JSkoCYNOmTZQvXz7Tez4+Pk61I6NSpUpRtWpVqlatytq1a6lTpw6NGzemVq1aea5bCCFcQeYACiGcFhAQQNWqVbn33nuzBH/ZadiwISdPniQ8PNwWIFm/SpQoQYkSJShbtiwHDhywnWM0Gjl06NBd66xTpw5ms9k2d+9O1h5Ik8lke61WrVr4+Phw4cKFLO2oUKECADVr1uS3337LVNevv/6a6z3eqUKFCjz77LNMmDDB4XOFECK/SAAohHCbXr16UapUKbp27cqePXs4e/YsO3fu5JVXXuHixYsAjBw5kpkzZ7Jhwwb++ecfhg0blmMOv0qVKtGvXz8GDhzIhg0bbHV+/fXXAFSsWBGVSsXGjRuJjY0lKSmJoKAgXnvtNUaNGsWqVas4ffo0f/zxBwsXLmTVqlUADB06lJMnTzJ27FhOnDjB6tWrWblypVP3PXLkSL7//nsOHjzo1PlCCOFqEgAKIdzG39+f3bt3c++999KjRw9q1qzJoEGDSEtLIzg4GIAxY8bQp08f+vXrR7NmzQgKCqJ79+451vvxxx/z1FNPMWzYMGrUqMHgwYNJTk4GoHz58kyZMoXx48dTpkwZRowYAcC0adN4++23mTFjBjVr1qRdu3Zs2rSJypUrA3Dvvffy7bffsmHDBurVq0dUVBTTp0936r5r1apF27ZtmThxolPnCyGEq6mUu82sFkIIIYQQRZL0AAohhBBCFDMSAAohhBBCFDMSAAohhBBCFDMSAAohhBBCFDMSAAohhBBCFDMSAAohhBBCFDMSAAohhBBCFDMSAAohhBBCFDMSAAohhBBCFDMSAAohhBBCFDMSAAohhBBCFDMSAAohhBBCFDP/B3KDhvUXwgWFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = np.sqrt(mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts))\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    fit_wls = sm.WLS(bin_correct_means, sm.add_constant(bin_prediction_means), weights=bin_counts).fit()\n",
    "    print(fit_wls.params)\n",
    "    y_regression = [fit_wls.params[0] + fit_wls.params[1]*x for x in bin_prediction_means]\n",
    "    plt.plot(bin_prediction_means, y_regression, label='Weighted Least Squares Regression', color=\"green\")\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration', color=\"#1f77b4\")\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration', color=\"#ff7f0e\")\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "for last_rating in (\"1\",\"2\",\"3\",\"4\"):\n",
    "    print(f\"Last rating: {last_rating}\")\n",
    "    plot_brier(dataset[dataset['r_history'].str.endswith(last_rating)]['p'], dataset[dataset['r_history'].str.endswith(last_rating)]['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "difficulty_calibration = pd.DataFrame(columns=['difficulty', 'predicted_retention', 'actual_retention'])\n",
    "difficulty_calibration = dataset[['difficulty', 'p', 'y']].copy()\n",
    "difficulty_calibration['bin'] = difficulty_calibration['difficulty'].map(round)\n",
    "difficulty_group = difficulty_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=difficulty_group.index, height=difficulty_group['y'],\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Difficulty\")\n",
    "lns.append(lns1)\n",
    "\n",
    "difficulty_group = difficulty_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(difficulty_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(difficulty_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  background-color: #ff1111;\n",
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       "#T_a3b91_row2_col8 {\n",
       "  background-color: #bdbdff;\n",
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       "#T_a3b91_row3_col6, #T_a3b91_row14_col2, #T_a3b91_row16_col2 {\n",
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       "  background-color: #ffd5d5;\n",
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       "  background-color: #b5b5ff;\n",
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       "  background-color: #ffeded;\n",
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       "  background-color: #d1d1ff;\n",
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       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
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       "#T_a3b91_row4_col8, #T_a3b91_row6_col8, #T_a3b91_row11_col8 {\n",
       "  background-color: #d5d5ff;\n",
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       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a3b91_row4_col12 {\n",
       "  background-color: #ffe1e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a3b91_row5_col3, #T_a3b91_row8_col5, #T_a3b91_row14_col4 {\n",
       "  background-color: #e1e1ff;\n",
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       "#T_a3b91_row5_col4 {\n",
       "  background-color: #ffb5b5;\n",
       "  color: #000000;\n",
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       "#T_a3b91_row5_col5, #T_a3b91_row5_col9 {\n",
       "  background-color: #cdcdff;\n",
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       "  background-color: #b9b9ff;\n",
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       "#T_a3b91_row5_col8, #T_a3b91_row6_col5, #T_a3b91_row9_col3, #T_a3b91_row9_col5, #T_a3b91_row9_col7 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
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       "#T_a3b91_row5_col10, #T_a3b91_row8_col6, #T_a3b91_row9_col4, #T_a3b91_row9_col6, #T_a3b91_row13_col5, #T_a3b91_row14_col0, #T_a3b91_row14_col5, #T_a3b91_row16_col1 {\n",
       "  background-color: #fff9f9;\n",
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       "  background-color: #ffcdcd;\n",
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       "  background-color: #ffd1d1;\n",
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       "  background-color: #ff9d9d;\n",
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       "  background-color: #ffb9b9;\n",
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       "  background-color: #ddddff;\n",
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       "  background-color: #a1a1ff;\n",
       "  color: #f1f1f1;\n",
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       "#T_a3b91_row7_col7, #T_a3b91_row7_col8, #T_a3b91_row9_col2, #T_a3b91_row10_col2, #T_a3b91_row11_col0, #T_a3b91_row12_col6, #T_a3b91_row16_col0 {\n",
       "  background-color: #fff1f1;\n",
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       "  background-color: #ffc9c9;\n",
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       "}\n",
       "#T_a3b91_row7_col11 {\n",
       "  background-color: #ff9999;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a3b91_row8_col1, #T_a3b91_row10_col6, #T_a3b91_row11_col2, #T_a3b91_row12_col5, #T_a3b91_row17_col0 {\n",
       "  background-color: #ffe5e5;\n",
       "  color: #000000;\n",
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       "#T_a3b91_row8_col2, #T_a3b91_row8_col3 {\n",
       "  background-color: #adadff;\n",
       "  color: #000000;\n",
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       "#T_a3b91_row8_col4, #T_a3b91_row9_col0, #T_a3b91_row9_col1, #T_a3b91_row13_col4 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
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       "#T_a3b91_row8_col7, #T_a3b91_row11_col4, #T_a3b91_row12_col7, #T_a3b91_row14_col3 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
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       "#T_a3b91_row8_col9, #T_a3b91_row10_col0, #T_a3b91_row10_col9, #T_a3b91_row12_col1, #T_a3b91_row12_col3, #T_a3b91_row15_col4 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
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       "#T_a3b91_row8_col10 {\n",
       "  background-color: #ff7979;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a3b91_row10_col8 {\n",
       "  background-color: #ffc1c1;\n",
       "  color: #000000;\n",
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       "#T_a3b91_row11_col3, #T_a3b91_row12_col2, #T_a3b91_row18_col0 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a3b91_row11_col5, #T_a3b91_row11_col7, #T_a3b91_row12_col4, #T_a3b91_row15_col0, #T_a3b91_row19_col2 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a3b91_row13_col6 {\n",
       "  background-color: #ffb1b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a3b91_row15_col3, #T_a3b91_row17_col1 {\n",
       "  background-color: #c1c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a3b91_row20_col0 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a3b91\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th id=\"T_a3b91_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_a3b91_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_a3b91_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_a3b91_level0_col3\" class=\"col_heading level0 col3\" >4</th>\n",
       "      <th id=\"T_a3b91_level0_col4\" class=\"col_heading level0 col4\" >5</th>\n",
       "      <th id=\"T_a3b91_level0_col5\" class=\"col_heading level0 col5\" >6</th>\n",
       "      <th id=\"T_a3b91_level0_col6\" class=\"col_heading level0 col6\" >7</th>\n",
       "      <th id=\"T_a3b91_level0_col7\" class=\"col_heading level0 col7\" >8</th>\n",
       "      <th id=\"T_a3b91_level0_col8\" class=\"col_heading level0 col8\" >9</th>\n",
       "      <th id=\"T_a3b91_level0_col9\" class=\"col_heading level0 col9\" >10</th>\n",
       "      <th id=\"T_a3b91_level0_col10\" class=\"col_heading level0 col10\" >11</th>\n",
       "      <th id=\"T_a3b91_level0_col11\" class=\"col_heading level0 col11\" >12</th>\n",
       "      <th id=\"T_a3b91_level0_col12\" class=\"col_heading level0 col12\" >13</th>\n",
       "      <th id=\"T_a3b91_level0_col13\" class=\"col_heading level0 col13\" >14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row0\" class=\"row_heading level0 row0\" >0.360000</th>\n",
       "      <td id=\"T_a3b91_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col7\" class=\"data row0 col7\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col8\" class=\"data row0 col8\" >-0.66%</td>\n",
       "      <td id=\"T_a3b91_row0_col9\" class=\"data row0 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col10\" class=\"data row0 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col11\" class=\"data row0 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col12\" class=\"data row0 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row0_col13\" class=\"data row0 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row1\" class=\"row_heading level0 row1\" >0.510000</th>\n",
       "      <td id=\"T_a3b91_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col6\" class=\"data row1 col6\" >2.27%</td>\n",
       "      <td id=\"T_a3b91_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col8\" class=\"data row1 col8\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col9\" class=\"data row1 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col10\" class=\"data row1 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col11\" class=\"data row1 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col12\" class=\"data row1 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row1_col13\" class=\"data row1 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row2\" class=\"row_heading level0 row2\" >0.710000</th>\n",
       "      <td id=\"T_a3b91_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_a3b91_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_a3b91_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_a3b91_row2_col3\" class=\"data row2 col3\" >1.25%</td>\n",
       "      <td id=\"T_a3b91_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_a3b91_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_a3b91_row2_col6\" class=\"data row2 col6\" >-1.02%</td>\n",
       "      <td id=\"T_a3b91_row2_col7\" class=\"data row2 col7\" >9.26%</td>\n",
       "      <td id=\"T_a3b91_row2_col8\" class=\"data row2 col8\" >-2.60%</td>\n",
       "      <td id=\"T_a3b91_row2_col9\" class=\"data row2 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row2_col10\" class=\"data row2 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row2_col11\" class=\"data row2 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row2_col12\" class=\"data row2 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row2_col13\" class=\"data row2 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row3\" class=\"row_heading level0 row3\" >1.000000</th>\n",
       "      <td id=\"T_a3b91_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_a3b91_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_a3b91_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_a3b91_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_a3b91_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_a3b91_row3_col5\" class=\"data row3 col5\" >-1.50%</td>\n",
       "      <td id=\"T_a3b91_row3_col6\" class=\"data row3 col6\" >1.56%</td>\n",
       "      <td id=\"T_a3b91_row3_col7\" class=\"data row3 col7\" >-3.57%</td>\n",
       "      <td id=\"T_a3b91_row3_col8\" class=\"data row3 col8\" ></td>\n",
       "      <td id=\"T_a3b91_row3_col9\" class=\"data row3 col9\" >-0.74%</td>\n",
       "      <td id=\"T_a3b91_row3_col10\" class=\"data row3 col10\" >1.68%</td>\n",
       "      <td id=\"T_a3b91_row3_col11\" class=\"data row3 col11\" >-2.83%</td>\n",
       "      <td id=\"T_a3b91_row3_col12\" class=\"data row3 col12\" >0.75%</td>\n",
       "      <td id=\"T_a3b91_row3_col13\" class=\"data row3 col13\" >2.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row4\" class=\"row_heading level0 row4\" >1.400000</th>\n",
       "      <td id=\"T_a3b91_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_a3b91_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_a3b91_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_a3b91_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_a3b91_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_a3b91_row4_col5\" class=\"data row4 col5\" >4.88%</td>\n",
       "      <td id=\"T_a3b91_row4_col6\" class=\"data row4 col6\" >-1.83%</td>\n",
       "      <td id=\"T_a3b91_row4_col7\" class=\"data row4 col7\" >-0.59%</td>\n",
       "      <td id=\"T_a3b91_row4_col8\" class=\"data row4 col8\" >-1.65%</td>\n",
       "      <td id=\"T_a3b91_row4_col9\" class=\"data row4 col9\" >0.42%</td>\n",
       "      <td id=\"T_a3b91_row4_col10\" class=\"data row4 col10\" >0.43%</td>\n",
       "      <td id=\"T_a3b91_row4_col11\" class=\"data row4 col11\" >0.45%</td>\n",
       "      <td id=\"T_a3b91_row4_col12\" class=\"data row4 col12\" >1.13%</td>\n",
       "      <td id=\"T_a3b91_row4_col13\" class=\"data row4 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row5\" class=\"row_heading level0 row5\" >1.960000</th>\n",
       "      <td id=\"T_a3b91_row5_col0\" class=\"data row5 col0\" ></td>\n",
       "      <td id=\"T_a3b91_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_a3b91_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_a3b91_row5_col3\" class=\"data row5 col3\" >-1.22%</td>\n",
       "      <td id=\"T_a3b91_row5_col4\" class=\"data row5 col4\" >2.89%</td>\n",
       "      <td id=\"T_a3b91_row5_col5\" class=\"data row5 col5\" >-2.01%</td>\n",
       "      <td id=\"T_a3b91_row5_col6\" class=\"data row5 col6\" >-2.69%</td>\n",
       "      <td id=\"T_a3b91_row5_col7\" class=\"data row5 col7\" >0.76%</td>\n",
       "      <td id=\"T_a3b91_row5_col8\" class=\"data row5 col8\" >-0.33%</td>\n",
       "      <td id=\"T_a3b91_row5_col9\" class=\"data row5 col9\" >-2.03%</td>\n",
       "      <td id=\"T_a3b91_row5_col10\" class=\"data row5 col10\" >0.23%</td>\n",
       "      <td id=\"T_a3b91_row5_col11\" class=\"data row5 col11\" >2.00%</td>\n",
       "      <td id=\"T_a3b91_row5_col12\" class=\"data row5 col12\" >1.74%</td>\n",
       "      <td id=\"T_a3b91_row5_col13\" class=\"data row5 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row6\" class=\"row_heading level0 row6\" >2.740000</th>\n",
       "      <td id=\"T_a3b91_row6_col0\" class=\"data row6 col0\" ></td>\n",
       "      <td id=\"T_a3b91_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_a3b91_row6_col2\" class=\"data row6 col2\" >3.82%</td>\n",
       "      <td id=\"T_a3b91_row6_col3\" class=\"data row6 col3\" >0.33%</td>\n",
       "      <td id=\"T_a3b91_row6_col4\" class=\"data row6 col4\" >2.75%</td>\n",
       "      <td id=\"T_a3b91_row6_col5\" class=\"data row6 col5\" >-0.34%</td>\n",
       "      <td id=\"T_a3b91_row6_col6\" class=\"data row6 col6\" >-1.35%</td>\n",
       "      <td id=\"T_a3b91_row6_col7\" class=\"data row6 col7\" >-1.85%</td>\n",
       "      <td id=\"T_a3b91_row6_col8\" class=\"data row6 col8\" >-1.64%</td>\n",
       "      <td id=\"T_a3b91_row6_col9\" class=\"data row6 col9\" >-1.28%</td>\n",
       "      <td id=\"T_a3b91_row6_col10\" class=\"data row6 col10\" >0.43%</td>\n",
       "      <td id=\"T_a3b91_row6_col11\" class=\"data row6 col11\" >3.86%</td>\n",
       "      <td id=\"T_a3b91_row6_col12\" class=\"data row6 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row6_col13\" class=\"data row6 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row7\" class=\"row_heading level0 row7\" >3.840000</th>\n",
       "      <td id=\"T_a3b91_row7_col0\" class=\"data row7 col0\" ></td>\n",
       "      <td id=\"T_a3b91_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_a3b91_row7_col2\" class=\"data row7 col2\" >-1.06%</td>\n",
       "      <td id=\"T_a3b91_row7_col3\" class=\"data row7 col3\" >-3.69%</td>\n",
       "      <td id=\"T_a3b91_row7_col4\" class=\"data row7 col4\" >-0.94%</td>\n",
       "      <td id=\"T_a3b91_row7_col5\" class=\"data row7 col5\" >-0.68%</td>\n",
       "      <td id=\"T_a3b91_row7_col6\" class=\"data row7 col6\" >-0.55%</td>\n",
       "      <td id=\"T_a3b91_row7_col7\" class=\"data row7 col7\" >0.53%</td>\n",
       "      <td id=\"T_a3b91_row7_col8\" class=\"data row7 col8\" >0.60%</td>\n",
       "      <td id=\"T_a3b91_row7_col9\" class=\"data row7 col9\" >1.29%</td>\n",
       "      <td id=\"T_a3b91_row7_col10\" class=\"data row7 col10\" >2.16%</td>\n",
       "      <td id=\"T_a3b91_row7_col11\" class=\"data row7 col11\" >3.99%</td>\n",
       "      <td id=\"T_a3b91_row7_col12\" class=\"data row7 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row7_col13\" class=\"data row7 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row8\" class=\"row_heading level0 row8\" >5.380000</th>\n",
       "      <td id=\"T_a3b91_row8_col0\" class=\"data row8 col0\" ></td>\n",
       "      <td id=\"T_a3b91_row8_col1\" class=\"data row8 col1\" >1.02%</td>\n",
       "      <td id=\"T_a3b91_row8_col2\" class=\"data row8 col2\" >-3.13%</td>\n",
       "      <td id=\"T_a3b91_row8_col3\" class=\"data row8 col3\" >-3.17%</td>\n",
       "      <td id=\"T_a3b91_row8_col4\" class=\"data row8 col4\" >-0.83%</td>\n",
       "      <td id=\"T_a3b91_row8_col5\" class=\"data row8 col5\" >-1.20%</td>\n",
       "      <td id=\"T_a3b91_row8_col6\" class=\"data row8 col6\" >0.16%</td>\n",
       "      <td id=\"T_a3b91_row8_col7\" class=\"data row8 col7\" >-0.17%</td>\n",
       "      <td id=\"T_a3b91_row8_col8\" class=\"data row8 col8\" >0.78%</td>\n",
       "      <td id=\"T_a3b91_row8_col9\" class=\"data row8 col9\" >0.82%</td>\n",
       "      <td id=\"T_a3b91_row8_col10\" class=\"data row8 col10\" >5.20%</td>\n",
       "      <td id=\"T_a3b91_row8_col11\" class=\"data row8 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row8_col12\" class=\"data row8 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row8_col13\" class=\"data row8 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row9\" class=\"row_heading level0 row9\" >7.530000</th>\n",
       "      <td id=\"T_a3b91_row9_col0\" class=\"data row9 col0\" >-0.94%</td>\n",
       "      <td id=\"T_a3b91_row9_col1\" class=\"data row9 col1\" >-0.84%</td>\n",
       "      <td id=\"T_a3b91_row9_col2\" class=\"data row9 col2\" >0.59%</td>\n",
       "      <td id=\"T_a3b91_row9_col3\" class=\"data row9 col3\" >-0.43%</td>\n",
       "      <td id=\"T_a3b91_row9_col4\" class=\"data row9 col4\" >0.17%</td>\n",
       "      <td id=\"T_a3b91_row9_col5\" class=\"data row9 col5\" >-0.40%</td>\n",
       "      <td id=\"T_a3b91_row9_col6\" class=\"data row9 col6\" >0.31%</td>\n",
       "      <td id=\"T_a3b91_row9_col7\" class=\"data row9 col7\" >-0.40%</td>\n",
       "      <td id=\"T_a3b91_row9_col8\" class=\"data row9 col8\" >0.77%</td>\n",
       "      <td id=\"T_a3b91_row9_col9\" class=\"data row9 col9\" >2.04%</td>\n",
       "      <td id=\"T_a3b91_row9_col10\" class=\"data row9 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row9_col11\" class=\"data row9 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row9_col12\" class=\"data row9 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row9_col13\" class=\"data row9 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row10\" class=\"row_heading level0 row10\" >10.540000</th>\n",
       "      <td id=\"T_a3b91_row10_col0\" class=\"data row10 col0\" >0.93%</td>\n",
       "      <td id=\"T_a3b91_row10_col1\" class=\"data row10 col1\" >-1.41%</td>\n",
       "      <td id=\"T_a3b91_row10_col2\" class=\"data row10 col2\" >0.58%</td>\n",
       "      <td id=\"T_a3b91_row10_col3\" class=\"data row10 col3\" >-0.70%</td>\n",
       "      <td id=\"T_a3b91_row10_col4\" class=\"data row10 col4\" >0.39%</td>\n",
       "      <td id=\"T_a3b91_row10_col5\" class=\"data row10 col5\" >0.47%</td>\n",
       "      <td id=\"T_a3b91_row10_col6\" class=\"data row10 col6\" >1.05%</td>\n",
       "      <td id=\"T_a3b91_row10_col7\" class=\"data row10 col7\" >1.35%</td>\n",
       "      <td id=\"T_a3b91_row10_col8\" class=\"data row10 col8\" >2.36%</td>\n",
       "      <td id=\"T_a3b91_row10_col9\" class=\"data row10 col9\" >0.91%</td>\n",
       "      <td id=\"T_a3b91_row10_col10\" class=\"data row10 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row10_col11\" class=\"data row10 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row10_col12\" class=\"data row10 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row10_col13\" class=\"data row10 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row11\" class=\"row_heading level0 row11\" >14.760000</th>\n",
       "      <td id=\"T_a3b91_row11_col0\" class=\"data row11 col0\" >0.47%</td>\n",
       "      <td id=\"T_a3b91_row11_col1\" class=\"data row11 col1\" >-1.37%</td>\n",
       "      <td id=\"T_a3b91_row11_col2\" class=\"data row11 col2\" >0.99%</td>\n",
       "      <td id=\"T_a3b91_row11_col3\" class=\"data row11 col3\" >-0.10%</td>\n",
       "      <td id=\"T_a3b91_row11_col4\" class=\"data row11 col4\" >-0.25%</td>\n",
       "      <td id=\"T_a3b91_row11_col5\" class=\"data row11 col5\" >0.14%</td>\n",
       "      <td id=\"T_a3b91_row11_col6\" class=\"data row11 col6\" >-1.44%</td>\n",
       "      <td id=\"T_a3b91_row11_col7\" class=\"data row11 col7\" >0.02%</td>\n",
       "      <td id=\"T_a3b91_row11_col8\" class=\"data row11 col8\" >-1.66%</td>\n",
       "      <td id=\"T_a3b91_row11_col9\" class=\"data row11 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row11_col10\" class=\"data row11 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row11_col11\" class=\"data row11 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row11_col12\" class=\"data row11 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row11_col13\" class=\"data row11 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row12\" class=\"row_heading level0 row12\" >20.660000</th>\n",
       "      <td id=\"T_a3b91_row12_col0\" class=\"data row12 col0\" >-0.54%</td>\n",
       "      <td id=\"T_a3b91_row12_col1\" class=\"data row12 col1\" >0.93%</td>\n",
       "      <td id=\"T_a3b91_row12_col2\" class=\"data row12 col2\" >-0.08%</td>\n",
       "      <td id=\"T_a3b91_row12_col3\" class=\"data row12 col3\" >0.82%</td>\n",
       "      <td id=\"T_a3b91_row12_col4\" class=\"data row12 col4\" >0.06%</td>\n",
       "      <td id=\"T_a3b91_row12_col5\" class=\"data row12 col5\" >0.95%</td>\n",
       "      <td id=\"T_a3b91_row12_col6\" class=\"data row12 col6\" >0.62%</td>\n",
       "      <td id=\"T_a3b91_row12_col7\" class=\"data row12 col7\" >-0.29%</td>\n",
       "      <td id=\"T_a3b91_row12_col8\" class=\"data row12 col8\" ></td>\n",
       "      <td id=\"T_a3b91_row12_col9\" class=\"data row12 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row12_col10\" class=\"data row12 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row12_col11\" class=\"data row12 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row12_col12\" class=\"data row12 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row12_col13\" class=\"data row12 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row13\" class=\"row_heading level0 row13\" >28.930000</th>\n",
       "      <td id=\"T_a3b91_row13_col0\" class=\"data row13 col0\" >-0.72%</td>\n",
       "      <td id=\"T_a3b91_row13_col1\" class=\"data row13 col1\" >-0.56%</td>\n",
       "      <td id=\"T_a3b91_row13_col2\" class=\"data row13 col2\" >1.33%</td>\n",
       "      <td id=\"T_a3b91_row13_col3\" class=\"data row13 col3\" >-0.59%</td>\n",
       "      <td id=\"T_a3b91_row13_col4\" class=\"data row13 col4\" >-0.90%</td>\n",
       "      <td id=\"T_a3b91_row13_col5\" class=\"data row13 col5\" >0.19%</td>\n",
       "      <td id=\"T_a3b91_row13_col6\" class=\"data row13 col6\" >3.04%</td>\n",
       "      <td id=\"T_a3b91_row13_col7\" class=\"data row13 col7\" ></td>\n",
       "      <td id=\"T_a3b91_row13_col8\" class=\"data row13 col8\" ></td>\n",
       "      <td id=\"T_a3b91_row13_col9\" class=\"data row13 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row13_col10\" class=\"data row13 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row13_col11\" class=\"data row13 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row13_col12\" class=\"data row13 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row13_col13\" class=\"data row13 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row14\" class=\"row_heading level0 row14\" >40.500000</th>\n",
       "      <td id=\"T_a3b91_row14_col0\" class=\"data row14 col0\" >0.23%</td>\n",
       "      <td id=\"T_a3b91_row14_col1\" class=\"data row14 col1\" >0.44%</td>\n",
       "      <td id=\"T_a3b91_row14_col2\" class=\"data row14 col2\" >1.48%</td>\n",
       "      <td id=\"T_a3b91_row14_col3\" class=\"data row14 col3\" >-0.19%</td>\n",
       "      <td id=\"T_a3b91_row14_col4\" class=\"data row14 col4\" >-1.17%</td>\n",
       "      <td id=\"T_a3b91_row14_col5\" class=\"data row14 col5\" >0.23%</td>\n",
       "      <td id=\"T_a3b91_row14_col6\" class=\"data row14 col6\" ></td>\n",
       "      <td id=\"T_a3b91_row14_col7\" class=\"data row14 col7\" ></td>\n",
       "      <td id=\"T_a3b91_row14_col8\" class=\"data row14 col8\" ></td>\n",
       "      <td id=\"T_a3b91_row14_col9\" class=\"data row14 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row14_col10\" class=\"data row14 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row14_col11\" class=\"data row14 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row14_col12\" class=\"data row14 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row14_col13\" class=\"data row14 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row15\" class=\"row_heading level0 row15\" >56.690000</th>\n",
       "      <td id=\"T_a3b91_row15_col0\" class=\"data row15 col0\" >0.12%</td>\n",
       "      <td id=\"T_a3b91_row15_col1\" class=\"data row15 col1\" >0.40%</td>\n",
       "      <td id=\"T_a3b91_row15_col2\" class=\"data row15 col2\" >1.60%</td>\n",
       "      <td id=\"T_a3b91_row15_col3\" class=\"data row15 col3\" >-2.35%</td>\n",
       "      <td id=\"T_a3b91_row15_col4\" class=\"data row15 col4\" >0.92%</td>\n",
       "      <td id=\"T_a3b91_row15_col5\" class=\"data row15 col5\" ></td>\n",
       "      <td id=\"T_a3b91_row15_col6\" class=\"data row15 col6\" ></td>\n",
       "      <td id=\"T_a3b91_row15_col7\" class=\"data row15 col7\" ></td>\n",
       "      <td id=\"T_a3b91_row15_col8\" class=\"data row15 col8\" ></td>\n",
       "      <td id=\"T_a3b91_row15_col9\" class=\"data row15 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row15_col10\" class=\"data row15 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row15_col11\" class=\"data row15 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row15_col12\" class=\"data row15 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row15_col13\" class=\"data row15 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row16\" class=\"row_heading level0 row16\" >79.370000</th>\n",
       "      <td id=\"T_a3b91_row16_col0\" class=\"data row16 col0\" >0.52%</td>\n",
       "      <td id=\"T_a3b91_row16_col1\" class=\"data row16 col1\" >0.17%</td>\n",
       "      <td id=\"T_a3b91_row16_col2\" class=\"data row16 col2\" >1.50%</td>\n",
       "      <td id=\"T_a3b91_row16_col3\" class=\"data row16 col3\" >-0.94%</td>\n",
       "      <td id=\"T_a3b91_row16_col4\" class=\"data row16 col4\" >0.45%</td>\n",
       "      <td id=\"T_a3b91_row16_col5\" class=\"data row16 col5\" ></td>\n",
       "      <td id=\"T_a3b91_row16_col6\" class=\"data row16 col6\" ></td>\n",
       "      <td id=\"T_a3b91_row16_col7\" class=\"data row16 col7\" ></td>\n",
       "      <td id=\"T_a3b91_row16_col8\" class=\"data row16 col8\" ></td>\n",
       "      <td id=\"T_a3b91_row16_col9\" class=\"data row16 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row16_col10\" class=\"data row16 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row16_col11\" class=\"data row16 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row16_col12\" class=\"data row16 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row16_col13\" class=\"data row16 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row17\" class=\"row_heading level0 row17\" >111.120000</th>\n",
       "      <td id=\"T_a3b91_row17_col0\" class=\"data row17 col0\" >1.03%</td>\n",
       "      <td id=\"T_a3b91_row17_col1\" class=\"data row17 col1\" >-2.40%</td>\n",
       "      <td id=\"T_a3b91_row17_col2\" class=\"data row17 col2\" >1.29%</td>\n",
       "      <td id=\"T_a3b91_row17_col3\" class=\"data row17 col3\" >1.95%</td>\n",
       "      <td id=\"T_a3b91_row17_col4\" class=\"data row17 col4\" ></td>\n",
       "      <td id=\"T_a3b91_row17_col5\" class=\"data row17 col5\" ></td>\n",
       "      <td id=\"T_a3b91_row17_col6\" class=\"data row17 col6\" ></td>\n",
       "      <td id=\"T_a3b91_row17_col7\" class=\"data row17 col7\" ></td>\n",
       "      <td id=\"T_a3b91_row17_col8\" class=\"data row17 col8\" ></td>\n",
       "      <td id=\"T_a3b91_row17_col9\" class=\"data row17 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row17_col10\" class=\"data row17 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row17_col11\" class=\"data row17 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row17_col12\" class=\"data row17 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row17_col13\" class=\"data row17 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row18\" class=\"row_heading level0 row18\" >155.570000</th>\n",
       "      <td id=\"T_a3b91_row18_col0\" class=\"data row18 col0\" >-0.07%</td>\n",
       "      <td id=\"T_a3b91_row18_col1\" class=\"data row18 col1\" >-3.55%</td>\n",
       "      <td id=\"T_a3b91_row18_col2\" class=\"data row18 col2\" >1.64%</td>\n",
       "      <td id=\"T_a3b91_row18_col3\" class=\"data row18 col3\" ></td>\n",
       "      <td id=\"T_a3b91_row18_col4\" class=\"data row18 col4\" ></td>\n",
       "      <td id=\"T_a3b91_row18_col5\" class=\"data row18 col5\" ></td>\n",
       "      <td id=\"T_a3b91_row18_col6\" class=\"data row18 col6\" ></td>\n",
       "      <td id=\"T_a3b91_row18_col7\" class=\"data row18 col7\" ></td>\n",
       "      <td id=\"T_a3b91_row18_col8\" class=\"data row18 col8\" ></td>\n",
       "      <td id=\"T_a3b91_row18_col9\" class=\"data row18 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row18_col10\" class=\"data row18 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row18_col11\" class=\"data row18 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row18_col12\" class=\"data row18 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row18_col13\" class=\"data row18 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row19\" class=\"row_heading level0 row19\" >217.800000</th>\n",
       "      <td id=\"T_a3b91_row19_col0\" class=\"data row19 col0\" >-1.50%</td>\n",
       "      <td id=\"T_a3b91_row19_col1\" class=\"data row19 col1\" ></td>\n",
       "      <td id=\"T_a3b91_row19_col2\" class=\"data row19 col2\" >0.01%</td>\n",
       "      <td id=\"T_a3b91_row19_col3\" class=\"data row19 col3\" ></td>\n",
       "      <td id=\"T_a3b91_row19_col4\" class=\"data row19 col4\" ></td>\n",
       "      <td id=\"T_a3b91_row19_col5\" class=\"data row19 col5\" ></td>\n",
       "      <td id=\"T_a3b91_row19_col6\" class=\"data row19 col6\" ></td>\n",
       "      <td id=\"T_a3b91_row19_col7\" class=\"data row19 col7\" ></td>\n",
       "      <td id=\"T_a3b91_row19_col8\" class=\"data row19 col8\" ></td>\n",
       "      <td id=\"T_a3b91_row19_col9\" class=\"data row19 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row19_col10\" class=\"data row19 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row19_col11\" class=\"data row19 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row19_col12\" class=\"data row19 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row19_col13\" class=\"data row19 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a3b91_level0_row20\" class=\"row_heading level0 row20\" >304.910000</th>\n",
       "      <td id=\"T_a3b91_row20_col0\" class=\"data row20 col0\" >-2.31%</td>\n",
       "      <td id=\"T_a3b91_row20_col1\" class=\"data row20 col1\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col2\" class=\"data row20 col2\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col3\" class=\"data row20 col3\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col4\" class=\"data row20 col4\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col5\" class=\"data row20 col5\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col6\" class=\"data row20 col6\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col7\" class=\"data row20 col7\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col8\" class=\"data row20 col8\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col9\" class=\"data row20 col9\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col10\" class=\"data row20 col10\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col11\" class=\"data row20 col11\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col12\" class=\"data row20 col12\" ></td>\n",
       "      <td id=\"T_a3b91_row20_col13\" class=\"data row20 col13\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2db0529a0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Comparision with Anki bulit-in algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anki(history):\n",
    "    ivl = 0\n",
    "    start_ease = 2.5\n",
    "    hard_interval = 1.2\n",
    "    ease_bonus = 1.3\n",
    "    graduating_interval = 1\n",
    "    easy_interval = 4\n",
    "    new_interval = 0\n",
    "    minimum_interval = 1\n",
    "    interval_modifier = 1\n",
    "    for i, (delta_t, rating) in enumerate(history):\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item()\n",
    "        if i == 0:\n",
    "            ease = start_ease\n",
    "            if rating == 4:\n",
    "                ivl = easy_interval\n",
    "            else:\n",
    "                ivl = graduating_interval\n",
    "        else:\n",
    "            delay = delta_t - ivl\n",
    "            if delay >= 0:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = ivl * hard_interval\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = (ivl + delay // 2) * ease\n",
    "                else:\n",
    "                    ivl = (ivl + delay) * ease * ease_bonus\n",
    "                    ease += 0.15\n",
    "            # early_review\n",
    "            else:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = max(delta_t * hard_interval / 2, ivl * hard_interval)\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = max(delta_t * ease, ivl)\n",
    "                else:\n",
    "                    ivl = max(delta_t * ease, ivl) * (0.5 * ease_bonus + 0.5)\n",
    "                    ease += 0.15\n",
    "            ivl = ivl * interval_modifier\n",
    "        ease = max(1.3, ease)\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "def sm2(history):\n",
    "    ivl = 0\n",
    "    ef = 2.5\n",
    "    reps = 0\n",
    "    for delta_t, rating in history:\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item() + 1\n",
    "        if rating > 2:\n",
    "            if reps == 0:\n",
    "                ivl = 1\n",
    "                reps = 1\n",
    "            elif reps == 1:\n",
    "                ivl = 6\n",
    "                reps = 2\n",
    "            else:\n",
    "                ivl = ivl * ef\n",
    "                reps += 1\n",
    "        else:\n",
    "            ivl = 1\n",
    "            reps = 0\n",
    "        ef = max(1.3, ef + (0.1 - (5 - rating) * (0.08 + (5 - rating) * 0.02)))\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "dataset['sm2_interval'] = dataset['tensor'].map(sm2)\n",
    "dataset['sm2_p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['sm2_interval'])\n",
    "cross_comparison = dataset[['sm2_p', 'p', 'y']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_Metric:  0.037127319024000016\n",
      "R-squared: -21.9800\n",
      "RMSE: 0.1412\n",
      "[0.76541153 0.15733363]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R_Metric = mean_squared_error(cross_comparison['y'], cross_comparison['sm2_p'], squared=False) - mean_squared_error(cross_comparison['y'], cross_comparison['p'], squared=False)\n",
    "print(\"R_Metric: \", R_Metric)\n",
    "plot_brier(dataset['sm2_p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Metric of FSRS: 0.0113\n",
      "Universal Metric of SM2: 0.0677\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "cross_comparison['SM2_B-W'] = cross_comparison['sm2_p'] - cross_comparison['y']\n",
    "cross_comparison['SM2_bin'] = cross_comparison['sm2_p'].map(lambda x: round(x, 1))\n",
    "cross_comparison['FSRS_B-W'] = cross_comparison['p'] - cross_comparison['y']\n",
    "cross_comparison['FSRS_bin'] = cross_comparison['p'].map(lambda x: round(x, 1))\n",
    "\n",
    "plt.axhline(y = 0.0, color = 'black', linestyle = '-')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='SM2_bin').agg({'y': ['mean'], 'FSRS_B-W': ['mean'], 'p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of FSRS: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['p', 'mean'], sample_weight=cross_comparison_group['p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['p', 'percent'] = cross_comparison_group['p', 'count'] / cross_comparison_group['p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['FSRS_B-W', 'mean'], s=cross_comparison_group['p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['FSRS_B-W', 'mean'], label='FSRS by SM2')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='FSRS_bin').agg({'y': ['mean'], 'SM2_B-W': ['mean'], 'sm2_p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of SM2: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['sm2_p', 'mean'], sample_weight=cross_comparison_group['sm2_p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['sm2_p', 'percent'] = cross_comparison_group['sm2_p', 'count'] / cross_comparison_group['sm2_p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['SM2_B-W', 'mean'], s=cross_comparison_group['sm2_p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['SM2_B-W', 'mean'], label='SM2 by FSRS')\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.grid(linestyle='--')\n",
    "plt.title(\"SM2 vs. FSRS\")\n",
    "plt.xlabel('Predicted R')\n",
    "plt.ylabel('B-W Metric')\n",
    "plt.xlim(0, 1)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsrs4anki",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
